IT Salary Survey

Dataset statistics

Number of variables	43
Number of observations	3009
Missing cells	74255
Missing cells (%)	57.4%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	1011.0 KiB
Average record size in memory	344.0 B

Variable types

Numeric	16
Categorical	24
Unsupported	2
Boolean	1

Warnings

`Timestamp` has a high cardinality: 2009 distinct values	High cardinality
`City` has a high cardinality: 183 distinct values	High cardinality
`Position` has a high cardinality: 148 distinct values	High cardinality
`Years of experience in Germany` has a high cardinality: 53 distinct values	High cardinality
`Your main technology / programming language` has a high cardinality: 274 distinct values	High cardinality
`Other technologies/programming languages you use often` has a high cardinality: 562 distinct values	High cardinality
`Yearly bonus + stocks in EUR` has a high cardinality: 168 distinct values	High cardinality
`Annual bonus+stocks one year ago. Only answer if staying in same country` has a high cardinality: 131 distinct values	High cardinality
`Company type` has a high cardinality: 101 distinct values	High cardinality
`Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR` has a high cardinality: 59 distinct values	High cardinality
`Zeitstempel` has a high cardinality: 991 distinct values	High cardinality
`Position (without seniority)` has a high cardinality: 51 distinct values	High cardinality
`Company name` has a high cardinality: 220 distinct values	High cardinality
`Company business sector` has a high cardinality: 52 distinct values	High cardinality
`Position` has a high cardinality: 397 distinct values	High cardinality
`Age` is highly correlated with `Years of experience`	High correlation
`Yearly brutto salary (without bonus and stocks) in EUR` is highly correlated with `Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country`	High correlation
`Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country` is highly correlated with `Yearly brutto salary (without bonus and stocks) in EUR`	High correlation
`Years of experience` is highly correlated with `Age`	High correlation
`Yearly brutto salary (without bonus and stocks)` is highly correlated with `Yearly bonus` and 1 other fields	High correlation
`Yearly bonus` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 3 other fields	High correlation
`Yearly stocks` is highly correlated with `Yearly bonus` and 1 other fields	High correlation
`Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country` is highly correlated with `Yearly brutto salary (without bonus and stocks)`	High correlation
`Yearly bonus one year ago. Only answer if staying in same country` is highly correlated with `Yearly bonus` and 1 other fields	High correlation
`Yearly stocks one year ago. Only answer if staying in same country` is highly correlated with `Yearly bonus` and 2 other fields	High correlation
`Current Salary` is highly correlated with `Salary one year ago` and 1 other fields	High correlation
`Salary one year ago` is highly correlated with `Current Salary` and 1 other fields	High correlation
`Salary two years ago` is highly correlated with `Current Salary` and 1 other fields	High correlation
`Age` is highly correlated with `Years of experience`	High correlation
`Yearly brutto salary (without bonus and stocks) in EUR` is highly correlated with `Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country`	High correlation
`Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country` is highly correlated with `Yearly brutto salary (without bonus and stocks) in EUR`	High correlation
`Years of experience` is highly correlated with `Age` and 3 other fields	High correlation
`Yearly brutto salary (without bonus and stocks)` is highly correlated with `Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country`	High correlation
`Yearly bonus` is highly correlated with `Yearly stocks` and 2 other fields	High correlation
`Yearly stocks` is highly correlated with `Yearly bonus` and 2 other fields	High correlation
`Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country` is highly correlated with `Years of experience` and 1 other fields	High correlation
`Yearly bonus one year ago. Only answer if staying in same country` is highly correlated with `Yearly bonus` and 2 other fields	High correlation
`Yearly stocks one year ago. Only answer if staying in same country` is highly correlated with `Yearly bonus` and 2 other fields	High correlation
`Current Salary` is highly correlated with `Salary one year ago` and 1 other fields	High correlation
`Salary one year ago` is highly correlated with `Years of experience` and 2 other fields	High correlation
`Salary two years ago` is highly correlated with `Years of experience` and 2 other fields	High correlation
`Yearly brutto salary (without bonus and stocks) in EUR` is highly correlated with `Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country`	High correlation
`Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country` is highly correlated with `Yearly brutto salary (without bonus and stocks) in EUR`	High correlation
`Yearly brutto salary (without bonus and stocks)` is highly correlated with `Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country`	High correlation
`Yearly bonus` is highly correlated with `Yearly stocks` and 1 other fields	High correlation
`Yearly stocks` is highly correlated with `Yearly bonus` and 2 other fields	High correlation
`Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country` is highly correlated with `Yearly brutto salary (without bonus and stocks)`	High correlation
`Yearly bonus one year ago. Only answer if staying in same country` is highly correlated with `Yearly bonus` and 2 other fields	High correlation
`Yearly stocks one year ago. Only answer if staying in same country` is highly correlated with `Yearly stocks` and 1 other fields	High correlation
`Current Salary` is highly correlated with `Salary one year ago` and 1 other fields	High correlation
`Salary one year ago` is highly correlated with `Current Salary` and 1 other fields	High correlation
`Salary two years ago` is highly correlated with `Current Salary` and 1 other fields	High correlation
`Yearly brutto salary (without bonus and stocks)` is highly correlated with `Position (without seniority)` and 5 other fields	High correlation
`Position (without seniority)` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 5 other fields	High correlation
`Employment status` is highly correlated with `Seniority level` and 3 other fields	High correlation
`Yearly stocks one year ago. Only answer if staying in same country` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 5 other fields	High correlation
`Age` is highly correlated with `Years of experience` and 2 other fields	High correlation
`Years of experience` is highly correlated with `Age` and 3 other fields	High correlation
`Company business sector` is highly correlated with `Position (without seniority)` and 1 other fields	High correlation
`Your level` is highly correlated with `Years of experience` and 1 other fields	High correlation
`Have you lost your job due to the coronavirus outbreak?` is highly correlated with `Years of experience in Germany` and 1 other fields	High correlation
`Yearly stocks` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 3 other fields	High correlation
`Seniority level` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 6 other fields	High correlation
`Years of experience in Germany` is highly correlated with `Employment status` and 5 other fields	High correlation
`Salary one year ago` is highly correlated with `Salary two years ago` and 1 other fields	High correlation
`Total years of experience` is highly correlated with `Employment status` and 4 other fields	High correlation
`Company size` is highly correlated with `Сontract duration`	High correlation
`Number of home office days per month` is highly correlated with `Yearly stocks one year ago. Only answer if staying in same country` and 2 other fields	High correlation
`Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR` is highly correlated with `Employment status` and 3 other fields	High correlation
`Сontract duration` is highly correlated with `Company size`	High correlation
`Salary two years ago` is highly correlated with `Your level` and 2 other fields	High correlation
`Main language at work` is highly correlated with `Position (without seniority)`	High correlation
`Yearly bonus one year ago. Only answer if staying in same country` is highly correlated with `Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country` and 1 other fields	High correlation
`Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 2 other fields	High correlation
`Yearly bonus` is highly correlated with `Yearly brutto salary (without bonus and stocks)` and 3 other fields	High correlation
`Current Salary` is highly correlated with `Years of experience` and 2 other fields	High correlation
`Timestamp` has 991 (32.9%) missing values	Missing
`Age` has 229 (7.6%) missing values	Missing
`Position` has 1762 (58.6%) missing values	Missing
`Total years of experience` has 1772 (58.9%) missing values	Missing
`Years of experience in Germany` has 1788 (59.4%) missing values	Missing
`Seniority level` has 792 (26.3%) missing values	Missing
`Your main technology / programming language` has 906 (30.1%) missing values	Missing
`Other technologies/programming languages you use often` has 1913 (63.6%) missing values	Missing
`Yearly brutto salary (without bonus and stocks) in EUR` has 1756 (58.4%) missing values	Missing
`Yearly bonus + stocks in EUR` has 2180 (72.4%) missing values	Missing
`Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country` has 2124 (70.6%) missing values	Missing
`Annual bonus+stocks one year ago. Only answer if staying in same country` has 2395 (79.6%) missing values	Missing
`Number of vacation days` has 893 (29.7%) missing values	Missing
`Employment status` has 1773 (58.9%) missing values	Missing
`Сontract duration` has 823 (27.4%) missing values	Missing
`Main language at work` has 36 (1.2%) missing values	Missing
`Company size` has 47 (1.6%) missing values	Missing
`Company type` has 91 (3.0%) missing values	Missing
`Have you lost your job due to the coronavirus outbreak?` has 1776 (59.0%) missing values	Missing
`Have you been forced to have a shorter working week (Kurzarbeit)? If yes, how many hours per week` has 2636 (87.6%) missing values	Missing
`Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR` has 2547 (84.6%) missing values	Missing
`Zeitstempel` has 2018 (67.1%) missing values	Missing
`Position (without seniority)` has 2019 (67.1%) missing values	Missing
`Years of experience` has 1286 (42.7%) missing values	Missing
`Yearly brutto salary (without bonus and stocks)` has 2019 (67.1%) missing values	Missing
`Yearly bonus` has 2479 (82.4%) missing values	Missing
`Yearly stocks` has 2806 (93.3%) missing values	Missing
`Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country` has 2406 (80.0%) missing values	Missing
`Yearly bonus one year ago. Only answer if staying in same country` has 2752 (91.5%) missing values	Missing
`Yearly stocks one year ago. Only answer if staying in same country` has 2870 (95.4%) missing values	Missing
`Number of home office days per month` has 2370 (78.8%) missing values	Missing
`Company name` has 2752 (91.5%) missing values	Missing
`Company business sector` has 2163 (71.9%) missing values	Missing
`0` has 3009 (100.0%) missing values	Missing
`Position` has 2272 (75.5%) missing values	Missing
`Your level` has 2266 (75.3%) missing values	Missing
`Current Salary` has 2259 (75.1%) missing values	Missing
`Salary one year ago` has 2413 (80.2%) missing values	Missing
`Salary two years ago` has 2546 (84.6%) missing values	Missing
`Are you getting any Stock Options?` has 2267 (75.3%) missing values	Missing
`Yearly brutto salary (without bonus and stocks) in EUR` is highly skewed (γ1 = 35.39641533)	Skewed
`Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country` is highly skewed (γ1 = 29.74879226)	Skewed
`Timestamp` is uniformly distributed	Uniform
`Zeitstempel` is uniformly distributed	Uniform
`Company name` is uniformly distributed	Uniform
`Number of vacation days` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`0` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Have you been forced to have a shorter working week (Kurzarbeit)? If yes, how many hours per week` has 197 (6.5%) zeros	Zeros
`Yearly bonus` has 123 (4.1%) zeros	Zeros
`Yearly stocks` has 31 (1.0%) zeros	Zeros
`Yearly stocks one year ago. Only answer if staying in same country` has 68 (2.3%) zeros	Zeros
`Number of home office days per month` has 101 (3.4%) zeros	Zeros

Reproduction

Analysis started	2021-06-26 16:22:28.073042
Analysis finished	2021-06-26 16:23:18.898725
Duration	50.83 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

Distinct	1253
Distinct (%)	41.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	520.8218677

Minimum	0
Maximum	1252
Zeros	3
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	50
Q1	250
median	501
Q3	752
95-th percentile	1101.6
Maximum	1252
Range	1252
Interquartile range (IQR)	502

Descriptive statistics

Standard deviation	321.8604404
Coefficient of variation (CV)	0.61798565
Kurtosis	-0.8368318496
Mean	520.8218677
Median Absolute Deviation (MAD)	251
Skewness	0.2923427676
Sum	1567153
Variance	103594.1431
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	3	0.1%
73	3	0.1%
95	3	0.1%
93	3	0.1%
91	3	0.1%
89	3	0.1%
87	3	0.1%
85	3	0.1%
83	3	0.1%
81	3	0.1%
Other values (1243)	2979	99.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3	0.1%
1	3	0.1%
2	3	0.1%
3	3	0.1%
4	3	0.1%
5	3	0.1%
6	3	0.1%
7	3	0.1%
8	3	0.1%
9	3	0.1%

Value	Count	Frequency (%)
1252	1	< 0.1%
1251	1	< 0.1%
1250	1	< 0.1%
1249	1	< 0.1%
1248	1	< 0.1%
1247	1	< 0.1%
1246	1	< 0.1%
1245	1	< 0.1%
1244	1	< 0.1%
1243	1	< 0.1%

Timestamp
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	2009
Distinct (%)	99.6%
Missing	991
Missing (%)	32.9%
Memory size	23.6 KiB

25/11/2020 18:28:01	2
14/12/2018 12:53:47	2
24/11/2020 14:07:23	2
14/12/2018 13:43:50	2
25/11/2020 08:47:37	2
Other values (2004)	2008

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	38342
Distinct characters	13
Distinct categories	3 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2000 ?
Unique (%)	99.1%

Sample

1st row	24/11/2020 11:14:15
2nd row	24/11/2020 11:14:16
3rd row	24/11/2020 11:14:21
4th row	24/11/2020 11:15:24
5th row	24/11/2020 11:15:46

Common Values

Value	Count	Frequency (%)
25/11/2020 18:28:01	2	0.1%
14/12/2018 12:53:47	2	0.1%
24/11/2020 14:07:23	2	0.1%
14/12/2018 13:43:50	2	0.1%
25/11/2020 08:47:37	2	0.1%
24/11/2020 13:55:19	2	0.1%
24/11/2020 15:07:35	2	0.1%
14/12/2018 14:35:19	2	0.1%
14/12/2018 13:44:09	2	0.1%
24/11/2020 15:04:23	1	< 0.1%
Other values (1999)	1999	66.4%
(Missing)	991	32.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
24/11/2020	467	11.6%
25/11/2020	253	6.3%
14/12/2018	252	6.2%
15/12/2018	121	3.0%
26/11/2020	91	2.3%
30/11/2020	86	2.1%
18/12/2018	72	1.8%
27/11/2020	72	1.8%
17/12/2018	56	1.4%
19/12/2018	47	1.2%
Other values (2086)	2519	62.4%

Most occurring characters

Value	Count	Frequency (%)
1	7417	19.3%
2	7032	18.3%
0	5208	13.6%
/	4036	10.5%
:	4036	10.5%
	2018	5.3%
4	1993	5.2%
5	1638	4.3%
3	1481	3.9%
8	1421	3.7%
Other values (3)	2062	5.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	28252	73.7%
Other Punctuation	8072	21.1%
Space Separator	2018	5.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	7417	26.3%
2	7032	24.9%
0	5208	18.4%
4	1993	7.1%
5	1638	5.8%
3	1481	5.2%
8	1421	5.0%
7	710	2.5%
9	678	2.4%
6	674	2.4%

Other Punctuation

Value	Count	Frequency (%)
/	4036	50.0%
:	4036	50.0%

Space Separator

Value	Count	Frequency (%)
	2018	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	38342	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	7417	19.3%
2	7032	18.3%
0	5208	13.6%
/	4036	10.5%
:	4036	10.5%
	2018	5.3%
4	1993	5.2%
5	1638	4.3%
3	1481	3.9%
8	1421	3.7%
Other values (3)	2062	5.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	38342	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	7417	19.3%
2	7032	18.3%
0	5208	13.6%
/	4036	10.5%
:	4036	10.5%
	2018	5.3%
4	1993	5.2%
5	1638	4.3%
3	1481	3.9%
8	1421	3.7%
Other values (3)	2062	5.4%

Age
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	41
Distinct (%)	1.5%
Missing	229
Missing (%)	7.6%
Infinite	0
Infinite (%)	0.0%
Mean	32.40107914

Minimum	20
Maximum	69
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	20
5-th percentile	25
Q1	29
median	32
Q3	35
95-th percentile	42
Maximum	69
Range	49
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	5.406931059
Coefficient of variation (CV)	0.1668750302
Kurtosis	2.808929424
Mean	32.40107914
Median Absolute Deviation (MAD)	3
Skewness	1.045417164
Sum	90075
Variance	29.23490348
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=41)

Value	Count	Frequency (%)
30	274	9.1%
33	231	7.7%
32	230	7.6%
31	218	7.2%
29	187	6.2%
28	181	6.0%
35	181	6.0%
34	171	5.7%
27	136	4.5%
36	126	4.2%
Other values (31)	845	28.1%
(Missing)	229	7.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
20	2	0.1%
21	6	0.2%
22	18	0.6%
23	25	0.8%
24	60	2.0%
25	95	3.2%
26	119	4.0%
27	136	4.5%
28	181	6.0%
29	187	6.2%

Value	Count	Frequency (%)
69	1	< 0.1%
66	1	< 0.1%
65	1	< 0.1%
60	1	< 0.1%
59	1	< 0.1%
56	2	0.1%
54	3	0.1%
53	1	< 0.1%
52	5	0.2%
51	2	0.1%

Gender
Categorical

Distinct	5
Distinct (%)	0.2%
Missing	24
Missing (%)	0.8%
Memory size	23.6 KiB

Male	1887
M	646
Female	345
F	105
Diverse	2

Length

Max length	7
Median length	4
Mean length	3.47839196
Min length	1

Characters and Unicode

Total characters	10383
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Male
4th row	Male
5th row	Male

Common Values

Value	Count	Frequency (%)
Male	1887	62.7%
M	646	21.5%
Female	345	11.5%
F	105	3.5%
Diverse	2	0.1%
(Missing)	24	0.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
male	1887	63.2%
m	646	21.6%
female	345	11.6%
f	105	3.5%
diverse	2	0.1%

Most occurring characters

Value	Count	Frequency (%)
e	2581	24.9%
M	2533	24.4%
a	2232	21.5%
l	2232	21.5%
F	450	4.3%
m	345	3.3%
D	2	< 0.1%
i	2	< 0.1%
v	2	< 0.1%
r	2	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7398	71.3%
Uppercase Letter	2985	28.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2581	34.9%
a	2232	30.2%
l	2232	30.2%
m	345	4.7%
i	2	< 0.1%
v	2	< 0.1%
r	2	< 0.1%
s	2	< 0.1%

Uppercase Letter

Value	Count	Frequency (%)
M	2533	84.9%
F	450	15.1%
D	2	0.1%

Most occurring scripts

Value	Count	Frequency (%)
Latin	10383	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2581	24.9%
M	2533	24.4%
a	2232	21.5%
l	2232	21.5%
F	450	4.3%
m	345	3.3%
D	2	< 0.1%
i	2	< 0.1%
v	2	< 0.1%
r	2	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10383	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2581	24.9%
M	2533	24.4%
a	2232	21.5%
l	2232	21.5%
F	450	4.3%
m	345	3.3%
D	2	< 0.1%
i	2	< 0.1%
v	2	< 0.1%
r	2	< 0.1%

City
Categorical

HIGH CARDINALITY

Distinct	183
Distinct (%)	6.1%
Missing	29
Missing (%)	1.0%
Memory size	23.6 KiB

Berlin	1402
Munich	476
München	249
Frankfurt	127
Amsterdam	104
Other values (178)	622

Length

Max length	21
Median length	6
Mean length	6.682214765
Min length	2

Characters and Unicode

Total characters	19913
Distinct characters	55
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	107 ?
Unique (%)	3.6%

Sample

1st row	Munich
2nd row	Berlin
3rd row	Berlin
4th row	Berlin
5th row	Berlin

Common Values

Value	Count	Frequency (%)
Berlin	1402	46.6%
Munich	476	15.8%
München	249	8.3%
Frankfurt	127	4.2%
Amsterdam	104	3.5%
Hamburg	90	3.0%
Stuttgart	56	1.9%
Cologne	36	1.2%
Köln	21	0.7%
Moscow	19	0.6%
Other values (173)	400	13.3%
(Missing)	29	1.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
berlin	1402	46.7%
munich	476	15.9%
münchen	249	8.3%
frankfurt	127	4.2%
amsterdam	106	3.5%
hamburg	90	3.0%
stuttgart	66	2.2%
cologne	36	1.2%
düsseldorf	21	0.7%
köln	21	0.7%
Other values (166)	408	13.6%

Most occurring characters

Value	Count	Frequency (%)
n	2811	14.1%
r	2187	11.0%
e	2099	10.5%
i	2048	10.3%
l	1619	8.1%
B	1435	7.2%
u	879	4.4%
h	800	4.0%
c	793	4.0%
M	767	3.9%
Other values (45)	4475	22.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	16837	84.6%
Uppercase Letter	3000	15.1%
Space Separator	60	0.3%
Dash Punctuation	7	< 0.1%
Other Punctuation	7	< 0.1%
Open Punctuation	1	< 0.1%
Close Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	2811	16.7%
r	2187	13.0%
e	2099	12.5%
i	2048	12.2%
l	1619	9.6%
u	879	5.2%
h	800	4.8%
c	793	4.7%
a	595	3.5%
t	580	3.4%
Other values (17)	2426	14.4%

Uppercase Letter

Value	Count	Frequency (%)
B	1435	47.8%
M	767	25.6%
F	133	4.4%
A	116	3.9%
H	115	3.8%
S	97	3.2%
K	67	2.2%
D	48	1.6%
C	43	1.4%
L	35	1.2%
Other values (13)	144	4.8%

Dash Punctuation

Value	Count	Frequency (%)
-	7	100.0%

Space Separator

Value	Count	Frequency (%)
	60	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	7	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	19837	99.6%
Common	76	0.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	2811	14.2%
r	2187	11.0%
e	2099	10.6%
i	2048	10.3%
l	1619	8.2%
B	1435	7.2%
u	879	4.4%
h	800	4.0%
c	793	4.0%
M	767	3.9%
Other values (40)	4399	22.2%

Common

Value	Count	Frequency (%)
	60	78.9%
-	7	9.2%
,	7	9.2%
(	1	1.3%
)	1	1.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	19602	98.4%
Latin 1 Sup	310	1.6%
Latin Ext A	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	2811	14.3%
r	2187	11.2%
e	2099	10.7%
i	2048	10.4%
l	1619	8.3%
B	1435	7.3%
u	879	4.5%
h	800	4.1%
c	793	4.0%
M	767	3.9%
Other values (42)	4164	21.2%

Latin 1 Sup

Value	Count	Frequency (%)
ü	288	92.9%
ö	22	7.1%

Latin Ext A

Value	Count	Frequency (%)
ń	1	100.0%

Position
Categorical

HIGH CARDINALITY
MISSING

Distinct	148
Distinct (%)	11.9%
Missing	1762
Missing (%)	58.6%
Memory size	23.6 KiB

Software Engineer	387
Backend Developer	174
Data Scientist	110
Frontend Developer	89
QA Engineer	71
Other values (143)	416

Length

Max length	44
Median length	17
Mean length	15.3648757
Min length	3

Characters and Unicode

Total characters	19160
Distinct characters	60
Distinct categories	8 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	119 ?
Unique (%)	9.5%

Sample

1st row	Software Engineer
2nd row	Backend Developer
3rd row	Software Engineer
4th row	Frontend Developer
5th row	Backend Developer

Common Values

Value	Count	Frequency (%)
Software Engineer	387	12.9%
Backend Developer	174	5.8%
Data Scientist	110	3.7%
Frontend Developer	89	3.0%
QA Engineer	71	2.4%
DevOps	57	1.9%
Mobile Developer	53	1.8%
ML Engineer	42	1.4%
Product Manager	39	1.3%
Data Engineer	25	0.8%
Other values (138)	200	6.6%
(Missing)	1762	58.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
engineer	551	22.4%
software	399	16.2%
developer	330	13.4%
backend	174	7.1%
data	156	6.3%
scientist	110	4.5%
frontend	89	3.6%
qa	75	3.1%
manager	74	3.0%
devops	58	2.4%
Other values (115)	443	18.0%

Most occurring characters

Value	Count	Frequency (%)
e	3273	17.1%
n	1812	9.5%
r	1618	8.4%
	1232	6.4%
a	1177	6.1%
t	1072	5.6%
o	992	5.2%
i	958	5.0%
g	693	3.6%
E	570	3.0%
Other values (50)	5763	30.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	15166	79.2%
Uppercase Letter	2693	14.1%
Space Separator	1232	6.4%
Other Punctuation	29	0.2%
Open Punctuation	18	0.1%
Close Punctuation	18	0.1%
Math Symbol	2	< 0.1%
Dash Punctuation	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3273	21.6%
n	1812	11.9%
r	1618	10.7%
a	1177	7.8%
t	1072	7.1%
o	992	6.5%
i	958	6.3%
g	693	4.6%
l	461	3.0%
f	411	2.7%
Other values (18)	2699	17.8%

Uppercase Letter

Value	Count	Frequency (%)
E	570	21.2%
D	567	21.1%
S	548	20.3%
B	190	7.1%
M	169	6.3%
A	121	4.5%
F	98	3.6%
Q	75	2.8%
O	68	2.5%
P	59	2.2%
Other values (13)	228	8.5%

Other Punctuation

Value	Count	Frequency (%)
/	24	82.8%
,	3	10.3%
&	1	3.4%
.	1	3.4%

Space Separator

Value	Count	Frequency (%)
	1232	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	18	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	18	100.0%

Math Symbol

Value	Count	Frequency (%)
+	2	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	17854	93.2%
Common	1301	6.8%
Cyrillic	5	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3273	18.3%
n	1812	10.1%
r	1618	9.1%
a	1177	6.6%
t	1072	6.0%
o	992	5.6%
i	958	5.4%
g	693	3.9%
E	570	3.2%
D	567	3.2%
Other values (37)	5122	28.7%

Common

Value	Count	Frequency (%)
	1232	94.7%
/	24	1.8%
(	18	1.4%
)	18	1.4%
,	3	0.2%
+	2	0.2%
-	2	0.2%
&	1	0.1%
.	1	0.1%

Cyrillic

Value	Count	Frequency (%)
и	2	40.0%
н	1	20.0%
у	1	20.0%
л	1	20.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	19155	> 99.9%
Cyrillic	5	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	3273	17.1%
n	1812	9.5%
r	1618	8.4%
	1232	6.4%
a	1177	6.1%
t	1072	5.6%
o	992	5.2%
i	958	5.0%
g	693	3.6%
E	570	3.0%
Other values (46)	5758	30.1%

Cyrillic

Value	Count	Frequency (%)
и	2	40.0%
н	1	20.0%
у	1	20.0%
л	1	20.0%

Total years of experience
Categorical

HIGH CORRELATION
MISSING

Distinct	48
Distinct (%)	3.9%
Missing	1772
Missing (%)	58.9%
Memory size	23.6 KiB

10	138
5	136
6	99
8	92
7	84
Other values (43)	688

Length

Max length	51
Median length	1
Mean length	1.528698464
Min length	1

Characters and Unicode

Total characters	1891
Distinct characters	39
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	1.2%

Sample

1st row	5
2nd row	7
3rd row	12
4th row	4
5th row	17

Common Values

Value	Count	Frequency (%)
10	138	4.6%
5	136	4.5%
6	99	3.3%
8	92	3.1%
7	84	2.8%
4	80	2.7%
12	67	2.2%
3	66	2.2%
15	63	2.1%
9	60	2.0%
Other values (38)	352	11.7%
(Missing)	1772	58.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
10	138	11.0%
5	136	10.8%
6	100	7.9%
8	93	7.4%
7	84	6.7%
4	80	6.3%
12	67	5.3%
3	66	5.2%
15	64	5.1%
9	60	4.8%
Other values (51)	372	29.5%

Most occurring characters

Value	Count	Frequency (%)
1	532	28.1%
5	224	11.8%
2	185	9.8%
0	179	9.5%
4	123	6.5%
6	122	6.5%
3	118	6.2%
8	113	6.0%
7	99	5.2%
9	68	3.6%
Other values (29)	128	6.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1763	93.2%
Lowercase Letter	74	3.9%
Space Separator	23	1.2%
Other Punctuation	21	1.1%
Uppercase Letter	6	0.3%
Open Punctuation	2	0.1%
Close Punctuation	2	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	12	16.2%
t	11	14.9%
s	10	13.5%
e	8	10.8%
n	7	9.5%
i	6	8.1%
r	3	4.1%
o	3	4.1%
l	3	4.1%
h	2	2.7%
Other values (7)	9	12.2%

Decimal Number

Value	Count	Frequency (%)
1	532	30.2%
5	224	12.7%
2	185	10.5%
0	179	10.2%
4	123	7.0%
6	122	6.9%
3	118	6.7%
8	113	6.4%
7	99	5.6%
9	68	3.9%

Uppercase Letter

Value	Count	Frequency (%)
Q	1	16.7%
A	1	16.7%
E	1	16.7%
C	1	16.7%
T	1	16.7%
O	1	16.7%

Other Punctuation

Value	Count	Frequency (%)
.	15	71.4%
,	5	23.8%
/	1	4.8%

Space Separator

Value	Count	Frequency (%)
	23	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	2	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1811	95.8%
Latin	80	4.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	12	15.0%
t	11	13.8%
s	10	12.5%
e	8	10.0%
n	7	8.8%
i	6	7.5%
r	3	3.8%
o	3	3.8%
l	3	3.8%
h	2	2.5%
Other values (13)	15	18.8%

Common

Value	Count	Frequency (%)
1	532	29.4%
5	224	12.4%
2	185	10.2%
0	179	9.9%
4	123	6.8%
6	122	6.7%
3	118	6.5%
8	113	6.2%
7	99	5.5%
9	68	3.8%
Other values (6)	48	2.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1891	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	532	28.1%
5	224	11.8%
2	185	9.8%
0	179	9.5%
4	123	6.5%
6	122	6.5%
3	118	6.2%
8	113	6.0%
7	99	5.2%
9	68	3.6%
Other values (29)	128	6.8%

Years of experience in Germany
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	53
Distinct (%)	4.3%
Missing	1788
Missing (%)	59.4%
Memory size	23.6 KiB

2	195
1	189
3	155
5	142
4	122
Other values (48)	418

Length

Max length	51
Median length	1
Mean length	1.305487305
Min length	1

Characters and Unicode

Total characters	1594
Distinct characters	39
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	24 ?
Unique (%)	2.0%

Sample

1st row	3
2nd row	4
3rd row	6
4th row	1
5th row	6

Common Values

Value	Count	Frequency (%)
2	195	6.5%
1	189	6.3%
3	155	5.2%
5	142	4.7%
4	122	4.1%
0	99	3.3%
6	70	2.3%
7	37	1.2%
10	29	1.0%
1.5	24	0.8%
Other values (43)	159	5.3%
(Missing)	1788	59.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2	195	15.7%
1	191	15.4%
3	157	12.6%
5	142	11.4%
4	124	10.0%
0	100	8.0%
6	71	5.7%
7	37	3.0%
10	29	2.3%
1.5	24	1.9%
Other values (51)	173	13.9%

Most occurring characters

Value	Count	Frequency (%)
1	300	18.8%
2	232	14.6%
5	225	14.1%
3	176	11.0%
0	165	10.4%
4	132	8.3%
6	74	4.6%
.	65	4.1%
7	39	2.4%
9	23	1.4%
Other values (29)	163	10.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1386	87.0%
Lowercase Letter	87	5.5%
Other Punctuation	84	5.3%
Space Separator	22	1.4%
Open Punctuation	4	0.3%
Close Punctuation	4	0.3%
Uppercase Letter	3	0.2%
Math Symbol	2	0.1%
Other Number	1	0.1%
Dash Punctuation	1	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	12	13.8%
a	12	13.8%
t	11	12.6%
s	9	10.3%
i	8	9.2%
e	6	6.9%
l	4	4.6%
o	4	4.6%
r	3	3.4%
d	3	3.4%
Other values (8)	15	17.2%

Decimal Number

Value	Count	Frequency (%)
1	300	21.6%
2	232	16.7%
5	225	16.2%
3	176	12.7%
0	165	11.9%
4	132	9.5%
6	74	5.3%
7	39	2.8%
9	23	1.7%
8	20	1.4%

Uppercase Letter

Value	Count	Frequency (%)
S	1	33.3%
G	1	33.3%
P	1	33.3%

Other Punctuation

Value	Count	Frequency (%)
.	65	77.4%
,	19	22.6%

Math Symbol

Value	Count	Frequency (%)
<	2	100.0%

Space Separator

Value	Count	Frequency (%)
	22	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	4	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	4	100.0%

Other Number

Value	Count	Frequency (%)
⁰	1	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1504	94.4%
Latin	90	5.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	12	13.3%
a	12	13.3%
t	11	12.2%
s	9	10.0%
i	8	8.9%
e	6	6.7%
l	4	4.4%
o	4	4.4%
r	3	3.3%
d	3	3.3%
Other values (11)	18	20.0%

Common

Value	Count	Frequency (%)
1	300	19.9%
2	232	15.4%
5	225	15.0%
3	176	11.7%
0	165	11.0%
4	132	8.8%
6	74	4.9%
.	65	4.3%
7	39	2.6%
9	23	1.5%
Other values (8)	73	4.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1593	99.9%
None	1	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	300	18.8%
2	232	14.6%
5	225	14.1%
3	176	11.0%
0	165	10.4%
4	132	8.3%
6	74	4.6%
.	65	4.1%
7	39	2.4%
9	23	1.4%
Other values (28)	162	10.2%

None

Value	Count	Frequency (%)
⁰	1	100.0%

Seniority level
Categorical

HIGH CORRELATION
MISSING

Distinct	24
Distinct (%)	1.1%
Missing	792
Missing (%)	26.3%
Memory size	23.6 KiB

Senior	1152
Middle	638
Lead	201
Junior	152
Head	50
Other values (19)	24

Length

Max length	41
Median length	6
Mean length	5.821831304
Min length	2

Characters and Unicode

Total characters	12907
Distinct characters	40
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18 ?
Unique (%)	0.8%

Sample

1st row	Senior
2nd row	Senior
3rd row	Lead
4th row	Junior
5th row	Senior

Common Values

Value	Count	Frequency (%)
Senior	1152	38.3%
Middle	638	21.2%
Lead	201	6.7%
Junior	152	5.1%
Head	50	1.7%
Principal	6	0.2%
Student	1	< 0.1%
C-level executive manager	1	< 0.1%
Intern	1	< 0.1%
No level	1	< 0.1%
Other values (14)	14	0.5%
(Missing)	792	26.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
senior	1152	51.6%
middle	638	28.6%
lead	201	9.0%
junior	152	6.8%
head	50	2.2%
principal	6	0.3%
no	4	0.2%
level	3	0.1%
manager	3	0.1%
student	3	0.1%
Other values (20)	22	1.0%

Most occurring characters

Value	Count	Frequency (%)
e	2075	16.1%
i	1961	15.2%
d	1532	11.9%
n	1327	10.3%
r	1325	10.3%
o	1312	10.2%
S	1155	8.9%
l	655	5.1%
M	640	5.0%
a	266	2.1%
Other values (30)	659	5.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	10664	82.6%
Uppercase Letter	2221	17.2%
Space Separator	19	0.1%
Dash Punctuation	2	< 0.1%
Other Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2075	19.5%
i	1961	18.4%
d	1532	14.4%
n	1327	12.4%
r	1325	12.4%
o	1312	12.3%
l	655	6.1%
a	266	2.5%
u	156	1.5%
t	14	0.1%
Other values (11)	41	0.4%

Uppercase Letter

Value	Count	Frequency (%)
S	1155	52.0%
M	640	28.8%
L	202	9.1%
J	152	6.8%
H	50	2.3%
P	7	0.3%
C	4	0.2%
N	2	0.1%
W	2	0.1%
V	1	< 0.1%
Other values (6)	6	0.3%

Space Separator

Value	Count	Frequency (%)
	19	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	12885	99.8%
Common	22	0.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2075	16.1%
i	1961	15.2%
d	1532	11.9%
n	1327	10.3%
r	1325	10.3%
o	1312	10.2%
S	1155	9.0%
l	655	5.1%
M	640	5.0%
a	266	2.1%
Other values (27)	637	4.9%

Common

Value	Count	Frequency (%)
	19	86.4%
-	2	9.1%
,	1	4.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	12907	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2075	16.1%
i	1961	15.2%
d	1532	11.9%
n	1327	10.3%
r	1325	10.3%
o	1312	10.2%
S	1155	8.9%
l	655	5.1%
M	640	5.0%
a	266	2.1%
Other values (30)	659	5.1%

Your main technology / programming language
Categorical

HIGH CARDINALITY
MISSING

Distinct	274
Distinct (%)	13.0%
Missing	906
Missing (%)	30.1%
Memory size	23.6 KiB

Python	369
Java	365
Not Relevant	119
Javascript / Typescript	113
PHP	108
Other values (269)	1029

Length

Max length	60
Median length	5
Mean length	7.038991916
Min length	1

Characters and Unicode

Total characters	14803
Distinct characters	70
Distinct categories	9 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	198 ?
Unique (%)	9.4%

Sample

1st row	TypeScript
2nd row	Ruby
3rd row	Javascript / Typescript
4th row	Javascript
5th row	C# .NET

Common Values

Value	Count	Frequency (%)
Python	369	12.3%
Java	365	12.1%
Not Relevant	119	4.0%
Javascript / Typescript	113	3.8%
PHP	108	3.6%
Kotlin	56	1.9%
.NET	54	1.8%
C/C++	46	1.5%
Swift	44	1.5%
AWS	43	1.4%
Other values (264)	786	26.1%
(Missing)	906	30.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
python	432	16.3%
java	391	14.8%
javascript	203	7.7%
typescript	152	5.7%
	133	5.0%
php	126	4.8%
relevant	119	4.5%
not	119	4.5%
c	97	3.7%
net	70	2.6%
Other values (198)	805	30.4%

Most occurring characters

Value	Count	Frequency (%)
a	1533	10.4%
t	1283	8.7%
o	802	5.4%
n	739	5.0%
v	738	5.0%
P	692	4.7%
y	655	4.4%
e	622	4.2%
J	615	4.2%
p	608	4.1%
Other values (60)	6516	44.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	10330	69.8%
Uppercase Letter	3235	21.9%
Space Separator	592	4.0%
Other Punctuation	429	2.9%
Math Symbol	195	1.3%
Dash Punctuation	11	0.1%
Decimal Number	5	< 0.1%
Open Punctuation	3	< 0.1%
Close Punctuation	3	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	1533	14.8%
t	1283	12.4%
o	802	7.8%
n	739	7.2%
v	738	7.1%
y	655	6.3%
e	622	6.0%
p	608	5.9%
i	580	5.6%
r	503	4.9%
Other values (18)	2267	21.9%

Uppercase Letter

Value	Count	Frequency (%)
P	692	21.4%
J	615	19.0%
S	329	10.2%
T	228	7.0%
R	219	6.8%
C	218	6.7%
N	199	6.2%
A	135	4.2%
H	122	3.8%
K	84	2.6%
Other values (17)	394	12.2%

Other Punctuation

Value	Count	Frequency (%)
/	206	48.0%
,	102	23.8%
.	78	18.2%
#	39	9.1%
&	2	0.5%
*	1	0.2%
:	1	0.2%

Decimal Number

Value	Count	Frequency (%)
8	3	60.0%
6	1	20.0%
3	1	20.0%

Space Separator

Value	Count	Frequency (%)
	592	100.0%

Math Symbol

Value	Count	Frequency (%)
+	195	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	11	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	3	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	13560	91.6%
Common	1238	8.4%
Cyrillic	5	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	1533	11.3%
t	1283	9.5%
o	802	5.9%
n	739	5.4%
v	738	5.4%
P	692	5.1%
y	655	4.8%
e	622	4.6%
J	615	4.5%
p	608	4.5%
Other values (41)	5273	38.9%

Common

Value	Count	Frequency (%)
	592	47.8%
/	206	16.6%
+	195	15.8%
,	102	8.2%
.	78	6.3%
#	39	3.2%
-	11	0.9%
8	3	0.2%
(	3	0.2%
)	3	0.2%
Other values (5)	6	0.5%

Cyrillic

Value	Count	Frequency (%)
ф	2	40.0%
С	1	20.0%
О	1	20.0%
м	1	20.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	14798	> 99.9%
Cyrillic	5	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	1533	10.4%
t	1283	8.7%
o	802	5.4%
n	739	5.0%
v	738	5.0%
P	692	4.7%
y	655	4.4%
e	622	4.2%
J	615	4.2%
p	608	4.1%
Other values (56)	6511	44.0%

Cyrillic

Value	Count	Frequency (%)
ф	2	40.0%
С	1	20.0%
О	1	20.0%
м	1	20.0%

Other technologies/programming languages you use often
Categorical

HIGH CARDINALITY
MISSING

Distinct	562
Distinct (%)	51.3%
Missing	1913
Missing (%)	63.6%
Memory size	23.6 KiB

Javascript / Typescript	44
Python	37
SQL	31
AWS, Docker	16
Kotlin	15
Other values (557)	953

Length

Max length	121
Median length	28
Mean length	30.83394161
Min length	2

Characters and Unicode

Total characters	33794
Distinct characters	59
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	407 ?
Unique (%)	37.1%

Sample

1st row	Kotlin, Javascript / Typescript
2nd row	Javascript / Typescript, Docker
3rd row	.NET, SQL, AWS, Docker
4th row	Python, AWS, Google Cloud, Kubernetes, Docker
5th row	Javascript / Typescript

Common Values

Value	Count	Frequency (%)
Javascript / Typescript	44	1.5%
Python	37	1.2%
SQL	31	1.0%
AWS, Docker	16	0.5%
Kotlin	15	0.5%
C/C++	13	0.4%
Swift	13	0.4%
Python, SQL	13	0.4%
Javascript / Typescript, SQL, AWS, Docker	12	0.4%
Python, C/C++	11	0.4%
Other values (552)	891	29.6%
(Missing)	1913	63.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	628	12.1%
docker	525	10.2%
sql	481	9.3%
python	409	7.9%
aws	401	7.8%
typescript	381	7.4%
javascript	381	7.4%
kubernetes	296	5.7%
scala	230	4.4%
java	229	4.4%
Other values (90)	1209	23.4%

Most occurring characters

Value	Count	Frequency (%)
	4078	12.1%
,	2651	7.8%
e	2113	6.3%
r	1751	5.2%
a	1735	5.1%
t	1671	4.9%
o	1667	4.9%
c	1544	4.6%
S	1186	3.5%
p	1164	3.4%
Other values (49)	14234	42.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	19167	56.7%
Uppercase Letter	6857	20.3%
Space Separator	4078	12.1%
Other Punctuation	3476	10.3%
Math Symbol	211	0.6%
Dash Punctuation	3	< 0.1%
Decimal Number	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2113	11.0%
r	1751	9.1%
a	1735	9.1%
t	1671	8.7%
o	1667	8.7%
c	1544	8.1%
p	1164	6.1%
s	1098	5.7%
i	952	5.0%
n	850	4.4%
Other values (16)	4622	24.1%

Uppercase Letter

Value	Count	Frequency (%)
S	1186	17.3%
P	634	9.2%
J	612	8.9%
A	561	8.2%
D	533	7.8%
L	487	7.1%
Q	486	7.1%
T	478	7.0%
K	418	6.1%
W	401	5.8%
Other values (13)	1061	15.5%

Other Punctuation

Value	Count	Frequency (%)
,	2651	76.3%
/	734	21.1%
.	88	2.5%
#	2	0.1%
;	1	< 0.1%

Decimal Number

Value	Count	Frequency (%)
3	1	50.0%
2	1	50.0%

Space Separator

Value	Count	Frequency (%)
	4078	100.0%

Math Symbol

Value	Count	Frequency (%)
+	211	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	26024	77.0%
Common	7770	23.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2113	8.1%
r	1751	6.7%
a	1735	6.7%
t	1671	6.4%
o	1667	6.4%
c	1544	5.9%
S	1186	4.6%
p	1164	4.5%
s	1098	4.2%
i	952	3.7%
Other values (39)	11143	42.8%

Common

Value	Count	Frequency (%)
	4078	52.5%
,	2651	34.1%
/	734	9.4%
+	211	2.7%
.	88	1.1%
-	3	< 0.1%
#	2	< 0.1%
3	1	< 0.1%
2	1	< 0.1%
;	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	33794	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	4078	12.1%
,	2651	7.8%
e	2113	6.3%
r	1751	5.2%
a	1735	5.1%
t	1671	4.9%
o	1667	4.9%
c	1544	4.6%
S	1186	3.5%
p	1164	3.4%
Other values (49)	14234	42.1%

Yearly brutto salary (without bonus and stocks) in EUR
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct	201
Distinct (%)	16.0%
Missing	1756
Missing (%)	58.4%
Infinite	0
Infinite (%)	0.0%
Mean	80279042.58

Minimum	10001
Maximum	1 × 10¹¹
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	10001
5-th percentile	40000
Q1	58800
median	70000
Q3	80000
95-th percentile	110000
Maximum	1 × 10¹¹
Range	9.999999 × 10¹⁰
Interquartile range (IQR)	21200

Descriptive statistics

Standard deviation	2825061108
Coefficient of variation (CV)	35.19051818
Kurtosis	1252.93722
Mean	80279042.58
Median Absolute Deviation (MAD)	10000
Skewness	35.39641533
Sum	1.005896404 × 10¹¹
Variance	7.980970262 × 10¹⁸
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
60000	85	2.8%
70000	83	2.8%
65000	77	2.6%
75000	73	2.4%
80000	66	2.2%
90000	45	1.5%
85000	42	1.4%
55000	35	1.2%
72000	32	1.1%
100000	28	0.9%
Other values (191)	687	22.8%
(Missing)	1756	58.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
10001	2	0.1%
10164	1	< 0.1%
11000	1	< 0.1%
11500	1	< 0.1%
12000	2	0.1%
13000	1	< 0.1%
14400	1	< 0.1%
14712	1	< 0.1%
16320	1	< 0.1%
17500	1	< 0.1%

Value	Count	Frequency (%)
1 × 10¹¹	1	< 0.1%
500000000	1	< 0.1%
850000	1	< 0.1%
300000	1	< 0.1%
250000	1	< 0.1%
240000	1	< 0.1%
200000	4	0.1%
180000	2	0.1%
172000	1	< 0.1%
160000	1	< 0.1%

Yearly bonus + stocks in EUR
Categorical

HIGH CARDINALITY
MISSING

Distinct	168
Distinct (%)	20.3%
Missing	2180
Missing (%)	72.4%
Memory size	23.6 KiB

0	227
5000	56
10000	45
2000	36
6000	26
Other values (163)	439

Length

Max length	10
Median length	4
Mean length	3.591073583
Min length	1

Characters and Unicode

Total characters	2977
Distinct characters	30
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	113 ?
Unique (%)	13.6%

Sample

1st row	5000
2nd row	120000
3rd row	5000
4th row	101
5th row	40000

Common Values

Value	Count	Frequency (%)
0	227	7.5%
5000	56	1.9%
10000	45	1.5%
2000	36	1.2%
6000	26	0.9%
3000	23	0.8%
1000	23	0.8%
20000	21	0.7%
4000	18	0.6%
15000	16	0.5%
Other values (158)	338	11.2%
(Missing)	2180	72.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
0	227	27.3%
5000	56	6.7%
10000	45	5.4%
2000	36	4.3%
6000	26	3.1%
3000	23	2.8%
1000	23	2.8%
20000	21	2.5%
4000	18	2.2%
15000	17	2.0%
Other values (159)	339	40.8%

Most occurring characters

Value	Count	Frequency (%)
0	2044	68.7%
5	203	6.8%
1	195	6.6%
2	130	4.4%
7	85	2.9%
6	71	2.4%
3	69	2.3%
4	66	2.2%
8	52	1.7%
9	32	1.1%
Other values (20)	30	1.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2947	99.0%
Lowercase Letter	21	0.7%
Other Punctuation	2	0.1%
Space Separator	2	0.1%
Dash Punctuation	2	0.1%
Uppercase Letter	2	0.1%
Math Symbol	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3	14.3%
o	2	9.5%
n	2	9.5%
d	2	9.5%
s	2	9.5%
b	1	4.8%
v	1	4.8%
g	1	4.8%
l	1	4.8%
y	1	4.8%
Other values (5)	5	23.8%

Decimal Number

Value	Count	Frequency (%)
0	2044	69.4%
5	203	6.9%
1	195	6.6%
2	130	4.4%
7	85	2.9%
6	71	2.4%
3	69	2.3%
4	66	2.2%
8	52	1.8%
9	32	1.1%

Other Punctuation

Value	Count	Frequency (%)
.	2	100.0%

Space Separator

Value	Count	Frequency (%)
	2	100.0%

Math Symbol

Value	Count	Frequency (%)
+	1	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2954	99.2%
Latin	23	0.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3	13.0%
o	2	8.7%
n	2	8.7%
d	2	8.7%
s	2	8.7%
N	2	8.7%
b	1	4.3%
v	1	4.3%
g	1	4.3%
l	1	4.3%
Other values (6)	6	26.1%

Common

Value	Count	Frequency (%)
0	2044	69.2%
5	203	6.9%
1	195	6.6%
2	130	4.4%
7	85	2.9%
6	71	2.4%
3	69	2.3%
4	66	2.2%
8	52	1.8%
9	32	1.1%
Other values (4)	7	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2977	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2044	68.7%
5	203	6.8%
1	195	6.6%
2	130	4.4%
7	85	2.9%
6	71	2.4%
3	69	2.3%
4	66	2.2%
8	52	1.7%
9	32	1.1%
Other values (20)	30	1.0%

Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct	147
Distinct (%)	16.6%
Missing	2124
Missing (%)	70.6%
Infinite	0
Infinite (%)	0.0%
Mean	632245.8723

Minimum	11000
Maximum	500000000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	11000
5-th percentile	37000
Q1	55000
median	65000
Q3	75000
95-th percentile	98000
Maximum	500000000
Range	499989000
Interquartile range (IQR)	20000

Descriptive statistics

Standard deviation	16805081.75
Coefficient of variation (CV)	26.57997859
Kurtosis	884.9937503
Mean	632245.8723
Median Absolute Deviation (MAD)	10000
Skewness	29.74879226
Sum	559537597
Variance	2.824107727 × 10¹⁴
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
65000	69	2.3%
60000	65	2.2%
75000	52	1.7%
70000	48	1.6%
55000	42	1.4%
80000	42	1.4%
72000	24	0.8%
85000	22	0.7%
50000	21	0.7%
90000	21	0.7%
Other values (137)	479	15.9%
(Missing)	2124	70.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
11000	1	< 0.1%
12000	2	0.1%
14712	1	< 0.1%
15000	1	< 0.1%
16000	2	0.1%
16560	1	< 0.1%
17000	1	< 0.1%
18000	1	< 0.1%
19000	1	< 0.1%
20000	2	0.1%

Value	Count	Frequency (%)
500000000	1	< 0.1%
760000	1	< 0.1%
230000	1	< 0.1%
200000	2	0.1%
190000	1	< 0.1%
169000	1	< 0.1%
160000	1	< 0.1%
156000	1	< 0.1%
140000	2	0.1%
132000	1	< 0.1%

Annual bonus+stocks one year ago. Only answer if staying in same country
Categorical

HIGH CARDINALITY
MISSING

Distinct	131
Distinct (%)	21.3%
Missing	2395
Missing (%)	79.6%
Memory size	23.6 KiB

0	200
5000	32
10000	25
60000	15
3000	13
Other values (126)	329

Length

Max length	31
Median length	4
Mean length	3.446254072
Min length	1

Characters and Unicode

Total characters	2116
Distinct characters	29
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	64 ?
Unique (%)	10.4%

Sample

1st row	10000
2nd row	5000
3rd row	100000
4th row	5000
5th row	13000

Common Values

Value	Count	Frequency (%)
0	200	6.6%
5000	32	1.1%
10000	25	0.8%
60000	15	0.5%
3000	13	0.4%
1000	13	0.4%
6000	12	0.4%
7000	12	0.4%
2000	11	0.4%
80000	10	0.3%
Other values (121)	271	9.0%
(Missing)	2395	79.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
0	200	32.4%
5000	32	5.2%
10000	25	4.0%
60000	15	2.4%
1000	13	2.1%
3000	13	2.1%
7000	12	1.9%
6000	12	1.9%
2000	11	1.8%
80000	10	1.6%
Other values (125)	275	44.5%

Most occurring characters

Value	Count	Frequency (%)
0	1462	69.1%
5	144	6.8%
1	104	4.9%
6	73	3.4%
2	68	3.2%
7	59	2.8%
3	56	2.6%
8	56	2.6%
4	44	2.1%
9	18	0.9%
Other values (19)	32	1.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2084	98.5%
Lowercase Letter	24	1.1%
Space Separator	4	0.2%
Other Punctuation	3	0.1%
Dash Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
g	4	16.7%
e	2	8.3%
n	2	8.3%
b	2	8.3%
d	2	8.3%
o	2	8.3%
l	1	4.2%
a	1	4.2%
r	1	4.2%
i	1	4.2%
Other values (6)	6	25.0%

Decimal Number

Value	Count	Frequency (%)
0	1462	70.2%
5	144	6.9%
1	104	5.0%
6	73	3.5%
2	68	3.3%
7	59	2.8%
3	56	2.7%
8	56	2.7%
4	44	2.1%
9	18	0.9%

Space Separator

Value	Count	Frequency (%)
	4	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	3	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2092	98.9%
Latin	24	1.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
g	4	16.7%
e	2	8.3%
n	2	8.3%
b	2	8.3%
d	2	8.3%
o	2	8.3%
l	1	4.2%
a	1	4.2%
r	1	4.2%
i	1	4.2%
Other values (6)	6	25.0%

Common

Value	Count	Frequency (%)
0	1462	69.9%
5	144	6.9%
1	104	5.0%
6	73	3.5%
2	68	3.3%
7	59	2.8%
3	56	2.7%
8	56	2.7%
4	44	2.1%
9	18	0.9%
Other values (3)	8	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	1462	69.1%
5	144	6.8%
1	104	4.9%
6	73	3.4%
2	68	3.2%
7	59	2.8%
3	56	2.6%
8	56	2.6%
4	44	2.1%
9	18	0.9%
Other values (19)	32	1.5%

Number of vacation days
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing	893
Missing (%)	29.7%
Memory size	23.6 KiB

Employment status
Categorical

HIGH CORRELATION
MISSING

Distinct	11
Distinct (%)	0.9%
Missing	1773
Missing (%)	58.9%
Memory size	23.6 KiB

Full-time employee	1190
Self-employed (freelancer)	28
Part-time employee	8
Founder	3
Werkstudent	1
Other values (6)	6

Length

Max length	79
Median length	18
Mean length	18.22168285
Min length	6

Characters and Unicode

Total characters	22522
Distinct characters	38
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	0.6%

Sample

1st row	Full-time employee
2nd row	Full-time employee
3rd row	Self-employed (freelancer)
4th row	Full-time employee
5th row	Full-time employee

Common Values

Value	Count	Frequency (%)
Full-time employee	1190	39.5%
Self-employed (freelancer)	28	0.9%
Part-time employee	8	0.3%
Founder	3	0.1%
Werkstudent	1	< 0.1%
Full-time position, part-time position, & self-employed (freelancing, tutoring)	1	< 0.1%
working student	1	< 0.1%
full-time, but 32 hours per week (it was my request, I'm a student)	1	< 0.1%
Company Director	1	< 0.1%
Working Student	1	< 0.1%
(Missing)	1773	58.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
employee	1198	48.2%
full-time	1192	48.0%
self-employed	29	1.2%
freelancer	28	1.1%
part-time	9	0.4%
founder	3	0.1%
student	3	0.1%
working	2	0.1%
position	2	0.1%
werkstudent	1	< 0.1%
Other values (17)	17	0.7%

Most occurring characters

Value	Count	Frequency (%)
e	4983	22.1%
l	3669	16.3%
m	2431	10.8%
	1248	5.5%
o	1240	5.5%
p	1232	5.5%
-	1230	5.5%
y	1229	5.5%
t	1227	5.4%
i	1211	5.4%
Other values (28)	2822	12.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	18738	83.2%
Space Separator	1248	5.5%
Uppercase Letter	1237	5.5%
Dash Punctuation	1230	5.5%
Open Punctuation	30	0.1%
Close Punctuation	30	0.1%
Other Punctuation	7	< 0.1%
Decimal Number	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	4983	26.6%
l	3669	19.6%
m	2431	13.0%
o	1240	6.6%
p	1232	6.6%
y	1229	6.6%
t	1227	6.5%
i	1211	6.5%
u	1203	6.4%
r	79	0.4%
Other values (12)	234	1.2%

Uppercase Letter

Value	Count	Frequency (%)
F	1194	96.5%
S	29	2.3%
P	8	0.6%
W	2	0.2%
I	2	0.2%
C	1	0.1%
D	1	0.1%

Other Punctuation

Value	Count	Frequency (%)
,	5	71.4%
&	1	14.3%
'	1	14.3%

Decimal Number

Value	Count	Frequency (%)
3	1	50.0%
2	1	50.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1230	100.0%

Space Separator

Value	Count	Frequency (%)
	1248	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	30	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	30	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	19975	88.7%
Common	2547	11.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	4983	24.9%
l	3669	18.4%
m	2431	12.2%
o	1240	6.2%
p	1232	6.2%
y	1229	6.2%
t	1227	6.1%
i	1211	6.1%
u	1203	6.0%
F	1194	6.0%
Other values (19)	356	1.8%

Common

Value	Count	Frequency (%)
	1248	49.0%
-	1230	48.3%
(	30	1.2%
)	30	1.2%
,	5	0.2%
&	1	< 0.1%
3	1	< 0.1%
2	1	< 0.1%
'	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	22522	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	4983	22.1%
l	3669	16.3%
m	2431	10.8%
	1248	5.5%
o	1240	5.5%
p	1232	5.5%
-	1230	5.5%
y	1229	5.5%
t	1227	5.4%
i	1211	5.4%
Other values (28)	2822	12.5%

Сontract duration
Categorical

HIGH CORRELATION
MISSING

Distinct	9
Distinct (%)	0.4%
Missing	823
Missing (%)	27.4%
Memory size	23.6 KiB

Unlimited contract	1159
unlimited	851
Temporary contract	64
more than 1 year	59
1 year	40
Other values (4)	13

Length

Max length	18
Median length	18
Mean length	14.16468435
Min length	1

Characters and Unicode

Total characters	30964
Distinct characters	23
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.1%

Sample

1st row	Unlimited contract
2nd row	Unlimited contract
3rd row	Temporary contract
4th row	Unlimited contract
5th row	Unlimited contract

Common Values

Value	Count	Frequency (%)
Unlimited contract	1159	38.5%
unlimited	851	28.3%
Temporary contract	64	2.1%
more than 1 year	59	2.0%
1 year	40	1.3%
6 months	7	0.2%
3 months	4	0.1%
0	1	< 0.1%
less than 3 months	1	< 0.1%
(Missing)	823	27.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
unlimited	2010	55.2%
contract	1223	33.6%
1	99	2.7%
year	99	2.7%
temporary	64	1.8%
than	60	1.6%
more	59	1.6%
months	12	0.3%
6	7	0.2%
3	5	0.1%
Other values (2)	2	0.1%

Most occurring characters

Value	Count	Frequency (%)
t	4528	14.6%
i	4020	13.0%
n	3305	10.7%
c	2446	7.9%
e	2233	7.2%
m	2145	6.9%
l	2011	6.5%
d	2010	6.5%
r	1509	4.9%
	1454	4.7%
Other values (13)	5303	17.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	28175	91.0%
Space Separator	1454	4.7%
Uppercase Letter	1223	3.9%
Decimal Number	112	0.4%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	4528	16.1%
i	4020	14.3%
n	3305	11.7%
c	2446	8.7%
e	2233	7.9%
m	2145	7.6%
l	2011	7.1%
d	2010	7.1%
r	1509	5.4%
a	1446	5.1%
Other values (6)	2522	9.0%

Decimal Number

Value	Count	Frequency (%)
1	99	88.4%
6	7	6.2%
3	5	4.5%
0	1	0.9%

Uppercase Letter

Value	Count	Frequency (%)
U	1159	94.8%
T	64	5.2%

Space Separator

Value	Count	Frequency (%)
	1454	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	29398	94.9%
Common	1566	5.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	4528	15.4%
i	4020	13.7%
n	3305	11.2%
c	2446	8.3%
e	2233	7.6%
m	2145	7.3%
l	2011	6.8%
d	2010	6.8%
r	1509	5.1%
a	1446	4.9%
Other values (8)	3745	12.7%

Common

Value	Count	Frequency (%)
	1454	92.8%
1	99	6.3%
6	7	0.4%
3	5	0.3%
0	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	30964	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	4528	14.6%
i	4020	13.0%
n	3305	10.7%
c	2446	7.9%
e	2233	7.2%
m	2145	6.9%
l	2011	6.5%
d	2010	6.5%
r	1509	4.9%
	1454	4.7%
Other values (13)	5303	17.1%

Main language at work
Categorical

HIGH CORRELATION
MISSING

Distinct	21
Distinct (%)	0.7%
Missing	36
Missing (%)	1.2%
Memory size	23.6 KiB

English	2354
Deutsch	316
German	186
Russian	78
French	8
Other values (16)	31

Length

Max length	37
Median length	7
Mean length	6.958964009
Min length	4

Characters and Unicode

Total characters	20689
Distinct characters	44
Distinct categories	7 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10 ?
Unique (%)	0.3%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	2354	78.2%
Deutsch	316	10.5%
German	186	6.2%
Russian	78	2.6%
French	8	0.3%
Polish	6	0.2%
Italian	5	0.2%
Spanish	4	0.1%
Русский	2	0.1%
English and German	2	0.1%
Other values (11)	12	0.4%
(Missing)	36	1.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	2358	79.0%
deutsch	316	10.6%
german	188	6.3%
russian	80	2.7%
french	8	0.3%
polish	6	0.2%
italian	5	0.2%
spanish	4	0.1%
	2	0.1%
czech	2	0.1%
Other values (12)	14	0.5%

Most occurring characters

Value	Count	Frequency (%)
s	2853	13.8%
h	2703	13.1%
n	2650	12.8%
i	2460	11.9%
l	2374	11.5%
g	2362	11.4%
E	2361	11.4%
e	519	2.5%
u	400	1.9%
c	331	1.6%
Other values (34)	1676	8.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	17685	85.5%
Uppercase Letter	2980	14.4%
Space Separator	10	< 0.1%
Other Punctuation	5	< 0.1%
Decimal Number	4	< 0.1%
Dash Punctuation	3	< 0.1%
Math Symbol	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
s	2853	16.1%
h	2703	15.3%
n	2650	15.0%
i	2460	13.9%
l	2374	13.4%
g	2362	13.4%
e	519	2.9%
u	400	2.3%
c	331	1.9%
t	326	1.8%
Other values (14)	707	4.0%

Uppercase Letter

Value	Count	Frequency (%)
E	2361	79.2%
D	320	10.7%
G	188	6.3%
R	80	2.7%
F	8	0.3%
P	7	0.2%
I	5	0.2%
S	4	0.1%
C	3	0.1%
Р	2	0.1%
Other values (2)	2	0.1%

Other Punctuation

Value	Count	Frequency (%)
/	2	40.0%
;	2	40.0%
,	1	20.0%

Decimal Number

Value	Count	Frequency (%)
5	2	50.0%
0	2	50.0%

Space Separator

Value	Count	Frequency (%)
	10	100.0%

Math Symbol

Value	Count	Frequency (%)
+	2	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	20651	99.8%
Common	24	0.1%
Cyrillic	14	0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
s	2853	13.8%
h	2703	13.1%
n	2650	12.8%
i	2460	11.9%
l	2374	11.5%
g	2362	11.4%
E	2361	11.4%
e	519	2.5%
u	400	1.9%
c	331	1.6%
Other values (20)	1638	7.9%

Common

Value	Count	Frequency (%)
	10	41.7%
-	3	12.5%
5	2	8.3%
0	2	8.3%
/	2	8.3%
+	2	8.3%
;	2	8.3%
,	1	4.2%

Cyrillic

Value	Count	Frequency (%)
с	4	28.6%
Р	2	14.3%
у	2	14.3%
к	2	14.3%
и	2	14.3%
й	2	14.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	20675	99.9%
Cyrillic	14	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
s	2853	13.8%
h	2703	13.1%
n	2650	12.8%
i	2460	11.9%
l	2374	11.5%
g	2362	11.4%
E	2361	11.4%
e	519	2.5%
u	400	1.9%
c	331	1.6%
Other values (28)	1662	8.0%

Cyrillic

Value	Count	Frequency (%)
с	4	28.6%
Р	2	14.3%
у	2	14.3%
к	2	14.3%
и	2	14.3%
й	2	14.3%

Company size
Categorical

HIGH CORRELATION
MISSING

Distinct	8
Distinct (%)	0.3%
Missing	47
Missing (%)	1.6%
Memory size	23.6 KiB

1000+	1013
100-1000	626
101-1000	405
50-100	252
10-50	222
Other values (3)	444

Length

Max length	8
Median length	6
Mean length	6.303511141
Min length	5

Characters and Unicode

Total characters	18671
Distinct characters	10
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	51-100
2nd row	101-1000
3rd row	101-1000
4th row	51-100
5th row	101-1000

Common Values

Value	Count	Frequency (%)
1000+	1013	33.7%
100-1000	626	20.8%
101-1000	405	13.5%
50-100	252	8.4%
10-50	222	7.4%
11-50	174	5.8%
51-100	147	4.9%
up to 10	123	4.1%
(Missing)	47	1.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1000	1013	31.6%
100-1000	626	19.5%
101-1000	405	12.6%
50-100	252	7.9%
10-50	222	6.9%
11-50	174	5.4%
51-100	147	4.6%
10	123	3.8%
up	123	3.8%
to	123	3.8%

Most occurring characters

Value	Count	Frequency (%)
0	9580	51.3%
1	4719	25.3%
-	1826	9.8%
+	1013	5.4%
5	795	4.3%
	246	1.3%
u	123	0.7%
p	123	0.7%
t	123	0.7%
o	123	0.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	15094	80.8%
Dash Punctuation	1826	9.8%
Math Symbol	1013	5.4%
Lowercase Letter	492	2.6%
Space Separator	246	1.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
u	123	25.0%
p	123	25.0%
t	123	25.0%
o	123	25.0%

Decimal Number

Value	Count	Frequency (%)
0	9580	63.5%
1	4719	31.3%
5	795	5.3%

Dash Punctuation

Value	Count	Frequency (%)
-	1826	100.0%

Math Symbol

Value	Count	Frequency (%)
+	1013	100.0%

Space Separator

Value	Count	Frequency (%)
	246	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	18179	97.4%
Latin	492	2.6%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	9580	52.7%
1	4719	26.0%
-	1826	10.0%
+	1013	5.6%
5	795	4.4%
	246	1.4%

Latin

Value	Count	Frequency (%)
u	123	25.0%
p	123	25.0%
t	123	25.0%
o	123	25.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	18671	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	9580	51.3%
1	4719	25.3%
-	1826	9.8%
+	1013	5.4%
5	795	4.3%
	246	1.3%
u	123	0.7%
p	123	0.7%
t	123	0.7%
o	123	0.7%

Company type
Categorical

HIGH CARDINALITY
MISSING

Distinct	101
Distinct (%)	3.5%
Missing	91
Missing (%)	3.0%
Memory size	23.6 KiB

Product	1830
Startup	578
Consulting / Agency	259
Agency	74
Bodyshop / Outsource	30
Other values (96)	147

Length

Max length	72
Median length	7
Mean length	8.393420151
Min length	3

Characters and Unicode

Total characters	24492
Distinct characters	49
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	74 ?
Unique (%)	2.5%

Sample

1st row	Product
2nd row	Product
3rd row	Product
4th row	Startup
5th row	Product

Common Values

Value	Count	Frequency (%)
Product	1830	60.8%
Startup	578	19.2%
Consulting / Agency	259	8.6%
Agency	74	2.5%
Bodyshop / Outsource	30	1.0%
Bank	11	0.4%
University	8	0.3%
Outsource	6	0.2%
Consulting	5	0.2%
Corporation	4	0.1%
Other values (91)	113	3.8%
(Missing)	91	3.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
product	1832	51.5%
startup	579	16.3%
agency	333	9.4%
	290	8.2%
consulting	271	7.6%
outsource	37	1.0%
bodyshop	30	0.8%
bank	13	0.4%
e-commerce	8	0.2%
university	8	0.2%
Other values (98)	153	4.3%

Most occurring characters

Value	Count	Frequency (%)
t	3403	13.9%
u	2808	11.5%
r	2541	10.4%
o	2304	9.4%
c	2279	9.3%
d	1888	7.7%
P	1841	7.5%
n	997	4.1%
a	666	2.7%
	651	2.7%
Other values (39)	5114	20.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	20285	82.8%
Uppercase Letter	3245	13.2%
Space Separator	651	2.7%
Other Punctuation	293	1.2%
Dash Punctuation	16	0.1%
Open Punctuation	1	< 0.1%
Close Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	3403	16.8%
u	2808	13.8%
r	2541	12.5%
o	2304	11.4%
c	2279	11.2%
d	1888	9.3%
n	997	4.9%
a	666	3.3%
p	630	3.1%
g	623	3.1%
Other values (14)	2146	10.6%

Uppercase Letter

Value	Count	Frequency (%)
P	1841	56.7%
S	586	18.1%
A	339	10.4%
C	289	8.9%
B	50	1.5%
O	45	1.4%
I	20	0.6%
T	17	0.5%
E	14	0.4%
U	10	0.3%
Other values (8)	34	1.0%

Other Punctuation

Value	Count	Frequency (%)
/	290	99.0%
,	2	0.7%
&	1	0.3%

Space Separator

Value	Count	Frequency (%)
	651	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	16	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	23530	96.1%
Common	962	3.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	3403	14.5%
u	2808	11.9%
r	2541	10.8%
o	2304	9.8%
c	2279	9.7%
d	1888	8.0%
P	1841	7.8%
n	997	4.2%
a	666	2.8%
p	630	2.7%
Other values (32)	4173	17.7%

Common

Value	Count	Frequency (%)
	651	67.7%
/	290	30.1%
-	16	1.7%
,	2	0.2%
&	1	0.1%
(	1	0.1%
)	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	24491	> 99.9%
Latin 1 Sup	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	3403	13.9%
u	2808	11.5%
r	2541	10.4%
o	2304	9.4%
c	2279	9.3%
d	1888	7.7%
P	1841	7.5%
n	997	4.1%
a	666	2.7%
	651	2.7%
Other values (38)	5113	20.9%

Latin 1 Sup

Value	Count	Frequency (%)
ö	1	100.0%

Have you lost your job due to the coronavirus outbreak?
Categorical

HIGH CORRELATION
MISSING

Distinct	10
Distinct (%)	0.8%
Missing	1776
Missing (%)	59.0%
Memory size	23.6 KiB

No	1162
Yes	63
i didn't but will be looking for new one because of covid	1
Laid off for a bit	1
Leads and project inquiries have slowed down	1
Other values (5)	5

Length

Max length	86
Median length	2
Mean length	2.334144363
Min length	2

Characters and Unicode

Total characters	2878
Distinct characters	39
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	0.6%

Sample

1st row	No
2nd row	No
3rd row	Yes
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	1162	38.6%
Yes	63	2.1%
i didn't but will be looking for new one because of covid	1	< 0.1%
Laid off for a bit	1	< 0.1%
Leads and project inquiries have slowed down	1	< 0.1%
Lost the job but for different reason	1	< 0.1%
kurzarbeitzeit for 1.5 months	1	< 0.1%
yes but found a new one with better pay / perks	1	< 0.1%
Have been a freelancer at the beginning of year	1	< 0.1%
No, but there was a salary cut at 10% for 3 months and then at 5% for further 3 months	1	< 0.1%
(Missing)	1776	59.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	1163	89.5%
yes	64	4.9%
for	6	0.5%
a	4	0.3%
but	4	0.3%
at	3	0.2%
months	3	0.2%
the	2	0.2%
and	2	0.2%
of	2	0.2%
Other values (43)	47	3.6%

Most occurring characters

Value	Count	Frequency (%)
o	1187	41.2%
N	1163	40.4%
e	98	3.4%
s	76	2.6%
	67	2.3%
Y	63	2.2%
t	26	0.9%
n	22	0.8%
a	22	0.8%
r	21	0.7%
Other values (29)	133	4.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1568	54.5%
Uppercase Letter	1230	42.7%
Space Separator	67	2.3%
Decimal Number	7	0.2%
Other Punctuation	6	0.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	1187	75.7%
e	98	6.2%
s	76	4.8%
t	26	1.7%
n	22	1.4%
a	22	1.4%
r	21	1.3%
i	16	1.0%
f	15	1.0%
b	12	0.8%
Other values (15)	73	4.7%

Other Punctuation

Value	Count	Frequency (%)
%	2	33.3%
'	1	16.7%
.	1	16.7%
,	1	16.7%
/	1	16.7%

Uppercase Letter

Value	Count	Frequency (%)
N	1163	94.6%
Y	63	5.1%
L	3	0.2%
H	1	0.1%

Decimal Number

Value	Count	Frequency (%)
1	2	28.6%
5	2	28.6%
3	2	28.6%
0	1	14.3%

Space Separator

Value	Count	Frequency (%)
	67	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2798	97.2%
Common	80	2.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	1187	42.4%
N	1163	41.6%
e	98	3.5%
s	76	2.7%
Y	63	2.3%
t	26	0.9%
n	22	0.8%
a	22	0.8%
r	21	0.8%
i	16	0.6%
Other values (19)	104	3.7%

Common

Value	Count	Frequency (%)
	67	83.8%
1	2	2.5%
5	2	2.5%
%	2	2.5%
3	2	2.5%
'	1	1.2%
.	1	1.2%
,	1	1.2%
0	1	1.2%
/	1	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2878	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	1187	41.2%
N	1163	40.4%
e	98	3.4%
s	76	2.6%
	67	2.3%
Y	63	2.2%
t	26	0.9%
n	22	0.8%
a	22	0.8%
r	21	0.7%
Other values (29)	133	4.6%

Have you been forced to have a shorter working week (Kurzarbeit)? If yes, how many hours per week
Real number (ℝ_≥0)

MISSING
ZEROS

Distinct	23
Distinct (%)	6.2%
Missing	2636
Missing (%)	87.6%
Infinite	0
Infinite (%)	0.0%
Mean	12.96782842

Minimum	0
Maximum	40
Zeros	197
Zeros (%)	6.5%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	30
95-th percentile	40
Maximum	40
Range	40
Interquartile range (IQR)	30

Descriptive statistics

Standard deviation	15.27517435
Coefficient of variation (CV)	1.177928475
Kurtosis	-1.430155043
Mean	12.96782842
Median Absolute Deviation (MAD)	0
Skewness	0.5374225668
Sum	4837
Variance	233.3309515
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=23)

Value	Count	Frequency (%)
0	197	6.5%
30	32	1.1%
32	31	1.0%
20	28	0.9%
40	27	0.9%
8	8	0.3%
24	8	0.3%
28	7	0.2%
10	6	0.2%
36	5	0.2%
Other values (13)	24	0.8%
(Missing)	2636	87.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	197	6.5%
2	1	< 0.1%
4	3	0.1%
5	2	0.1%
8	8	0.3%
10	6	0.2%
12	1	< 0.1%
15	1	< 0.1%
16	1	< 0.1%
20	28	0.9%

Value	Count	Frequency (%)
40	27	0.9%
39	1	< 0.1%
38	1	< 0.1%
37.5	2	0.1%
36	5	0.2%
35	4	0.1%
34	2	0.1%
32	31	1.0%
30	32	1.1%
28	7	0.2%

Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	59
Distinct (%)	12.8%
Missing	2547
Missing (%)	84.6%
Memory size	23.6 KiB

0	161
500	47
No	33
1000	23
600	20
Other values (54)	178

Length

Max length	55
Median length	3
Mean length	2.582251082
Min length	1

Characters and Unicode

Total characters	1193
Distinct characters	44
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	31 ?
Unique (%)	6.7%

Sample

1st row	600
2nd row	1000
3rd row	200
4th row	1100
5th row	1

Common Values

Value	Count	Frequency (%)
0	161	5.4%
500	47	1.6%
No	33	1.1%
1000	23	0.8%
600	20	0.7%
200	19	0.6%
1500	19	0.6%
300	18	0.6%
700	14	0.5%
400	12	0.4%
Other values (49)	96	3.2%
(Missing)	2547	84.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
0	161	33.3%
500	47	9.7%
no	40	8.3%
1000	23	4.8%
600	21	4.3%
200	20	4.1%
1500	19	3.9%
300	18	3.7%
700	14	2.9%
400	13	2.7%
Other values (60)	107	22.2%

Most occurring characters

Value	Count	Frequency (%)
0	663	55.6%
5	96	8.0%
1	77	6.5%
o	52	4.4%
2	39	3.3%
N	33	2.8%
6	29	2.4%
4	25	2.1%
3	22	1.8%
	22	1.8%
Other values (34)	135	11.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	992	83.2%
Lowercase Letter	136	11.4%
Uppercase Letter	36	3.0%
Space Separator	22	1.8%
Other Punctuation	5	0.4%
Currency Symbol	1	0.1%
Dash Punctuation	1	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	52	38.2%
n	15	11.0%
e	14	10.3%
t	9	6.6%
m	8	5.9%
r	5	3.7%
h	4	2.9%
v	4	2.9%
a	4	2.9%
l	3	2.2%
Other values (14)	18	13.2%

Decimal Number

Value	Count	Frequency (%)
0	663	66.8%
5	96	9.7%
1	77	7.8%
2	39	3.9%
6	29	2.9%
4	25	2.5%
3	22	2.2%
7	21	2.1%
8	16	1.6%
9	4	0.4%

Other Punctuation

Value	Count	Frequency (%)
%	2	40.0%
/	1	20.0%
.	1	20.0%
,	1	20.0%

Uppercase Letter

Value	Count	Frequency (%)
N	33	91.7%
I	2	5.6%
A	1	2.8%

Currency Symbol

Value	Count	Frequency (%)
€	1	100.0%

Space Separator

Value	Count	Frequency (%)
	22	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1021	85.6%
Latin	172	14.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	52	30.2%
N	33	19.2%
n	15	8.7%
e	14	8.1%
t	9	5.2%
m	8	4.7%
r	5	2.9%
h	4	2.3%
v	4	2.3%
a	4	2.3%
Other values (17)	24	14.0%

Common

Value	Count	Frequency (%)
0	663	64.9%
5	96	9.4%
1	77	7.5%
2	39	3.8%
6	29	2.8%
4	25	2.4%
3	22	2.2%
	22	2.2%
7	21	2.1%
8	16	1.6%
Other values (7)	11	1.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1192	99.9%
Currency Symbols	1	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	663	55.6%
5	96	8.1%
1	77	6.5%
o	52	4.4%
2	39	3.3%
N	33	2.8%
6	29	2.4%
4	25	2.1%
3	22	1.8%
	22	1.8%
Other values (33)	134	11.2%

Currency Symbols

Value	Count	Frequency (%)
€	1	100.0%

Zeitstempel
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	991
Distinct (%)	100.0%
Missing	2018
Missing (%)	67.1%
Memory size	23.6 KiB

02.12.2019 17:50:19	1
02.12.2019 15:44:44	1
04.12.2019 21:53:33	1
02.12.2019 11:32:59	1
02.12.2019 11:49:02	1
Other values (986)	986

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	18829
Distinct characters	13
Distinct categories	3 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	991 ?
Unique (%)	100.0%

Sample

1st row	02.12.2019 11:18:26
2nd row	02.12.2019 11:18:35
3rd row	02.12.2019 11:18:56
4th row	02.12.2019 11:19:08
5th row	02.12.2019 11:19:37

Common Values

Value	Count	Frequency (%)
02.12.2019 17:50:19	1	< 0.1%
02.12.2019 15:44:44	1	< 0.1%
04.12.2019 21:53:33	1	< 0.1%
02.12.2019 11:32:59	1	< 0.1%
02.12.2019 11:49:02	1	< 0.1%
02.12.2019 19:48:29	1	< 0.1%
03.12.2019 12:03:11	1	< 0.1%
06.12.2019 14:18:14	1	< 0.1%
06.12.2019 10:19:22	1	< 0.1%
10.12.2019 17:42:56	1	< 0.1%
Other values (981)	981	32.6%
(Missing)	2018	67.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
02.12.2019	266	13.4%
04.12.2019	126	6.4%
03.12.2019	119	6.0%
05.12.2019	98	4.9%
10.12.2019	67	3.4%
11.12.2019	47	2.4%
20.12.2019	45	2.3%
06.12.2019	40	2.0%
09.12.2019	20	1.0%
08.12.2019	17	0.9%
Other values (1012)	1137	57.4%

Most occurring characters

Value	Count	Frequency (%)
1	3585	19.0%
2	3190	16.9%
0	2658	14.1%
.	1982	10.5%
:	1982	10.5%
9	1293	6.9%
	991	5.3%
3	779	4.1%
5	741	3.9%
4	728	3.9%
Other values (3)	900	4.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	13874	73.7%
Other Punctuation	3964	21.1%
Space Separator	991	5.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	3585	25.8%
2	3190	23.0%
0	2658	19.2%
9	1293	9.3%
3	779	5.6%
5	741	5.3%
4	728	5.2%
6	338	2.4%
8	285	2.1%
7	277	2.0%

Other Punctuation

Value	Count	Frequency (%)
.	1982	50.0%
:	1982	50.0%

Space Separator

Value	Count	Frequency (%)
	991	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	18829	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	3585	19.0%
2	3190	16.9%
0	2658	14.1%
.	1982	10.5%
:	1982	10.5%
9	1293	6.9%
	991	5.3%
3	779	4.1%
5	741	3.9%
4	728	3.9%
Other values (3)	900	4.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	18829	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	3585	19.0%
2	3190	16.9%
0	2658	14.1%
.	1982	10.5%
:	1982	10.5%
9	1293	6.9%
	991	5.3%
3	779	4.1%
5	741	3.9%
4	728	3.9%
Other values (3)	900	4.8%

Position (without seniority)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	51
Distinct (%)	5.2%
Missing	2019
Missing (%)	67.1%
Memory size	23.6 KiB

Backend Developer	253
Data Scientist	122
Fullstack Developer	78
Frontend Developer	68
Manager	63
Other values (46)	406

Length

Max length	29
Median length	17
Mean length	14.51111111
Min length	2

Characters and Unicode

Total characters	14366
Distinct characters	50
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	22 ?
Unique (%)	2.2%

Sample

1st row	Fullstack Developer
2nd row	Backend Developer
3rd row	Mobile Developer
4th row	Backend Developer
5th row	Embedded Developer

Common Values

Value	Count	Frequency (%)
Backend Developer	253	8.4%
Data Scientist	122	4.1%
Fullstack Developer	78	2.6%
Frontend Developer	68	2.3%
Manager	63	2.1%
QA	56	1.9%
DevOps	53	1.8%
Mobile Developer	46	1.5%
Data Engineer	39	1.3%
Software Architect	36	1.2%
Other values (41)	176	5.8%
(Missing)	2019	67.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
developer	467	25.5%
backend	254	13.8%
data	162	8.8%
scientist	122	6.7%
engineer	92	5.0%
fullstack	78	4.3%
manager	74	4.0%
frontend	68	3.7%
qa	56	3.1%
devops	54	2.9%
Other values (53)	407	22.2%

Most occurring characters

Value	Count	Frequency (%)
e	2483	17.3%
a	994	6.9%
n	946	6.6%
r	895	6.2%
	846	5.9%
t	768	5.3%
l	715	5.0%
D	695	4.8%
o	682	4.7%
c	594	4.1%
Other values (40)	4748	33.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	11435	79.6%
Uppercase Letter	2043	14.2%
Space Separator	846	5.9%
Open Punctuation	14	0.1%
Close Punctuation	14	0.1%
Other Punctuation	13	0.1%
Decimal Number	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2483	21.7%
a	994	8.7%
n	946	8.3%
r	895	7.8%
t	768	6.7%
l	715	6.3%
o	682	6.0%
c	594	5.2%
i	536	4.7%
p	535	4.7%
Other values (13)	2287	20.0%

Uppercase Letter

Value	Count	Frequency (%)
D	695	34.0%
B	274	13.4%
S	203	9.9%
M	155	7.6%
F	153	7.5%
A	133	6.5%
E	125	6.1%
O	75	3.7%
Q	56	2.7%
L	40	2.0%
Other values (10)	134	6.6%

Other Punctuation

Value	Count	Frequency (%)
,	11	84.6%
/	1	7.7%
&	1	7.7%

Space Separator

Value	Count	Frequency (%)
	846	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	14	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	14	100.0%

Decimal Number

Value	Count	Frequency (%)
3	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	13478	93.8%
Common	888	6.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2483	18.4%
a	994	7.4%
n	946	7.0%
r	895	6.6%
t	768	5.7%
l	715	5.3%
D	695	5.2%
o	682	5.1%
c	594	4.4%
i	536	4.0%
Other values (33)	4170	30.9%

Common

Value	Count	Frequency (%)
	846	95.3%
(	14	1.6%
)	14	1.6%
,	11	1.2%
/	1	0.1%
3	1	0.1%
&	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	14366	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2483	17.3%
a	994	6.9%
n	946	6.6%
r	895	6.2%
	846	5.9%
t	768	5.3%
l	715	5.0%
D	695	4.8%
o	682	4.7%
c	594	4.1%
Other values (40)	4748	33.1%

Years of experience
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	33
Distinct (%)	1.9%
Missing	1286
Missing (%)	42.7%
Infinite	0
Infinite (%)	0.0%
Mean	8.537724898

Minimum	0
Maximum	38
Zeros	9
Zeros (%)	0.3%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	5
median	8
Q3	11
95-th percentile	18
Maximum	38
Range	38
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	4.906947591
Coefficient of variation (CV)	0.5747371401
Kurtosis	1.436354784
Mean	8.537724898
Median Absolute Deviation (MAD)	3
Skewness	0.8812288078
Sum	14710.5
Variance	24.07813466
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
10	213	7.1%
5	164	5.5%
8	148	4.9%
7	135	4.5%
6	123	4.1%
3	108	3.6%
4	103	3.4%
12	97	3.2%
9	91	3.0%
15	90	3.0%
Other values (23)	451	15.0%
(Missing)	1286	42.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	9	0.3%
0.5	2	0.1%
1	62	2.1%
1.5	2	0.1%
2	72	2.4%
2.5	5	0.2%
3	108	3.6%
4	103	3.4%
4.5	1	< 0.1%
5	164	5.5%

Value	Count	Frequency (%)
38	1	< 0.1%
30	3	0.1%
28	2	0.1%
25	8	0.3%
24	1	< 0.1%
22	4	0.1%
21	1	< 0.1%
20	36	1.2%
19	11	0.4%
18	28	0.9%

Yearly brutto salary (without bonus and stocks)
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	173
Distinct (%)	17.5%
Missing	2019
Missing (%)	67.1%
Infinite	0
Infinite (%)	0.0%
Mean	72562.21212

Minimum	6000
Maximum	216000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	6000
5-th percentile	42000
Q1	60000
median	70000
Q3	80000
95-th percentile	118650
Maximum	216000
Range	210000
Interquartile range (IQR)	20000

Descriptive statistics

Standard deviation	23947.37918
Coefficient of variation (CV)	0.3300254841
Kurtosis	5.61555162
Mean	72562.21212
Median Absolute Deviation (MAD)	10000
Skewness	1.534901428
Sum	71836590
Variance	573476969.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
70000	63	2.1%
65000	59	2.0%
60000	53	1.8%
75000	53	1.8%
80000	37	1.2%
55000	33	1.1%
72000	27	0.9%
68000	25	0.8%
62000	24	0.8%
90000	22	0.7%
Other values (163)	594	19.7%
(Missing)	2019	67.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6000	1	< 0.1%
13200	1	< 0.1%
14000	1	< 0.1%
14400	1	< 0.1%
15600	1	< 0.1%
20000	1	< 0.1%
21600	2	0.1%
22000	1	< 0.1%
23500	1	< 0.1%
24000	2	0.1%

Value	Count	Frequency (%)
216000	1	< 0.1%
210000	2	0.1%
200000	1	< 0.1%
190000	1	< 0.1%
175000	1	< 0.1%
170000	1	< 0.1%
160000	3	0.1%
155000	1	< 0.1%
150000	4	0.1%
140000	2	0.1%

Yearly bonus
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct	91
Distinct (%)	17.2%
Missing	2479
Missing (%)	82.4%
Infinite	0
Infinite (%)	0.0%
Mean	7857.169811

Minimum	0
Maximum	80000
Zeros	123
Zeros (%)	4.1%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	300
median	5000
Q3	10000
95-th percentile	25000
Maximum	80000
Range	80000
Interquartile range (IQR)	9700

Descriptive statistics

Standard deviation	10320.62834
Coefficient of variation (CV)	1.31353001
Kurtosis	12.85681305
Mean	7857.169811
Median Absolute Deviation (MAD)	5000
Skewness	2.975022003
Sum	4164300
Variance	106515369.4
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	123	4.1%
5000	56	1.9%
3000	30	1.0%
10000	26	0.9%
6000	20	0.7%
7000	17	0.6%
12000	16	0.5%
25000	16	0.5%
4000	15	0.5%
20000	14	0.5%
Other values (81)	197	6.5%
(Missing)	2479	82.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	123	4.1%
1	5	0.2%
10	2	0.1%
23	1	< 0.1%
200	1	< 0.1%
300	4	0.1%
600	1	< 0.1%
800	1	< 0.1%
1000	6	0.2%
1200	1	< 0.1%

Value	Count	Frequency (%)
80000	2	0.1%
60000	1	< 0.1%
55000	3	0.1%
48000	1	< 0.1%
47000	1	< 0.1%
46000	1	< 0.1%
45000	1	< 0.1%
43000	1	< 0.1%
37000	1	< 0.1%
36000	3	0.1%

Yearly stocks
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct	61
Distinct (%)	30.0%
Missing	2806
Missing (%)	93.3%
Infinite	0
Infinite (%)	0.0%
Mean	18263.1198

Minimum	0
Maximum	750000
Zeros	31
Zeros (%)	1.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2000
Q3	20000
95-th percentile	71300
Maximum	750000
Range	750000
Interquartile range (IQR)	19999

Descriptive statistics

Standard deviation	61012.38777
Coefficient of variation (CV)	3.340742898
Kurtosis	107.0964767
Mean	18263.1198
Median Absolute Deviation (MAD)	2000
Skewness	9.526380439
Sum	3707413.32
Variance	3722511462
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	43	1.4%
0	31	1.0%
10000	8	0.3%
2000	8	0.3%
25000	7	0.2%
50000	6	0.2%
5000	6	0.2%
3000	6	0.2%
30000	5	0.2%
20000	5	0.2%
Other values (51)	78	2.6%
(Missing)	2806	93.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	31	1.0%
0.01	2	0.1%
0.1	3	0.1%
1	43	1.4%
5	1	< 0.1%
10	1	< 0.1%
30	1	< 0.1%
50	1	< 0.1%
100	1	< 0.1%
125	1	< 0.1%

Value	Count	Frequency (%)
750000	1	< 0.1%
350000	1	< 0.1%
140000	1	< 0.1%
117000	1	< 0.1%
100000	1	< 0.1%
90000	1	< 0.1%
84000	1	< 0.1%
80000	1	< 0.1%
75000	1	< 0.1%
72000	2	0.1%

Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	128
Distinct (%)	21.2%
Missing	2406
Missing (%)	80.0%
Infinite	0
Infinite (%)	0.0%
Mean	65803.89552

Minimum	0
Maximum	200000
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	38100
Q1	55000
median	65000
Q3	75000
95-th percentile	99900
Maximum	200000
Range	200000
Interquartile range (IQR)	20000

Descriptive statistics

Standard deviation	20554.01761
Coefficient of variation (CV)	0.3123525963
Kurtosis	5.331724798
Mean	65803.89552
Median Absolute Deviation (MAD)	10000
Skewness	1.032919331
Sum	39679749
Variance	422467640.1
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
60000	49	1.6%
65000	38	1.3%
55000	38	1.3%
75000	33	1.1%
70000	32	1.1%
66000	17	0.6%
80000	15	0.5%
50000	14	0.5%
52000	14	0.5%
72000	13	0.4%
Other values (118)	340	11.3%
(Missing)	2406	80.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	1	< 0.1%
58	1	< 0.1%
7200	1	< 0.1%
9000	1	< 0.1%
10000	1	< 0.1%
17000	1	< 0.1%
19600	1	< 0.1%
20000	2	0.1%
22730	1	< 0.1%

Value	Count	Frequency (%)
200000	1	< 0.1%
160000	2	0.1%
146000	1	< 0.1%
144000	1	< 0.1%
130000	2	0.1%
127000	1	< 0.1%
120000	2	0.1%
117000	1	< 0.1%
115000	5	0.2%
114000	1	< 0.1%

Yearly bonus one year ago. Only answer if staying in same country
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	64
Distinct (%)	24.9%
Missing	2752
Missing (%)	91.5%
Infinite	0
Infinite (%)	0.0%
Mean	8144

Minimum	0
Maximum	150000
Zeros	23
Zeros (%)	0.8%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1000
median	5000
Q3	9000
95-th percentile	26000
Maximum	150000
Range	150000
Interquartile range (IQR)	8000

Descriptive statistics

Standard deviation	15067.8767
Coefficient of variation (CV)	1.850181324
Kurtosis	43.01728724
Mean	8144
Median Absolute Deviation (MAD)	4000
Skewness	5.754762713
Sum	2093008
Variance	227040908.3
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5000	27	0.9%
1	26	0.9%
0	23	0.8%
3000	17	0.6%
10000	17	0.6%
8000	12	0.4%
6000	12	0.4%
4000	10	0.3%
12000	9	0.3%
7000	9	0.3%
Other values (54)	95	3.2%
(Missing)	2752	91.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	23	0.8%
1	26	0.9%
16	1	< 0.1%
35	1	< 0.1%
200	1	< 0.1%
300	2	0.1%
500	4	0.1%
800	1	< 0.1%
900	1	< 0.1%
1000	5	0.2%

Value	Count	Frequency (%)
150000	1	< 0.1%
118000	1	< 0.1%
73000	1	< 0.1%
60000	1	< 0.1%
59000	1	< 0.1%
50000	2	0.1%
45000	1	< 0.1%
43500	1	< 0.1%
35000	1	< 0.1%
30000	3	0.1%

Yearly stocks one year ago. Only answer if staying in same country
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct	31
Distinct (%)	22.3%
Missing	2870
Missing (%)	95.4%
Infinite	0
Infinite (%)	0.0%
Mean	8204.476331

Minimum	0
Maximum	520000
Zeros	68
Zeros (%)	2.3%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0.1
Q3	3000
95-th percentile	20500
Maximum	520000
Range	520000
Interquartile range (IQR)	3000

Descriptive statistics

Standard deviation	45224.39737
Coefficient of variation (CV)	5.512161356
Kurtosis	121.1445566
Mean	8204.476331
Median Absolute Deviation (MAD)	0.1
Skewness	10.70644724
Sum	1140422.21
Variance	2045246118
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
0	68	2.3%
1	19	0.6%
10000	8	0.3%
15000	5	0.2%
3000	4	0.1%
20000	4	0.1%
5000	4	0.1%
2000	2	0.1%
50000	2	0.1%
0.1	2	0.1%
Other values (21)	21	0.7%
(Missing)	2870	95.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	68	2.3%
0.01	1	< 0.1%
0.1	2	0.1%
1	19	0.6%
5	1	< 0.1%
10	1	< 0.1%
38	1	< 0.1%
100	1	< 0.1%
350	1	< 0.1%
360	1	< 0.1%

Value	Count	Frequency (%)
520000	1	< 0.1%
80000	1	< 0.1%
60000	1	< 0.1%
50000	2	0.1%
37000	1	< 0.1%
25000	1	< 0.1%
20000	4	0.1%
18240	1	< 0.1%
15000	5	0.2%
12000	1	< 0.1%

Number of home office days per month
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct	21
Distinct (%)	3.3%
Missing	2370
Missing (%)	78.8%
Infinite	0
Infinite (%)	0.0%
Mean	6.462050078

Minimum	0
Maximum	365
Zeros	101
Zeros (%)	3.4%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	4
Q3	7
95-th percentile	20
Maximum	365
Range	365
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	15.38460138
Coefficient of variation (CV)	2.380761708
Kurtosis	463.7945219
Mean	6.462050078
Median Absolute Deviation (MAD)	2
Skewness	19.97055126
Sum	4129.25
Variance	236.6859596
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=21)

Value	Count	Frequency (%)
4	162	5.4%
0	101	3.4%
5	101	3.4%
20	65	2.2%
2	47	1.6%
10	34	1.1%
8	30	1.0%
1	26	0.9%
3	23	0.8%
15	16	0.5%
Other values (11)	34	1.1%
(Missing)	2370	78.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	101	3.4%
0.25	1	< 0.1%
1	26	0.9%
2	47	1.6%
3	23	0.8%
4	162	5.4%
5	101	3.4%
6	12	0.4%
7	10	0.3%
8	30	1.0%

Value	Count	Frequency (%)
365	1	< 0.1%
30	1	< 0.1%
25	1	< 0.1%
24	1	< 0.1%
20	65	2.2%
16	1	< 0.1%
15	16	0.5%
14	1	< 0.1%
12	2	0.1%
10	34	1.1%

Company name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	220
Distinct (%)	85.6%
Missing	2752
Missing (%)	91.5%
Memory size	23.6 KiB

Zalando	7
Auto1	5
Booking.com	4
Sixt	3
ING	3
Other values (215)	235

Length

Max length	46
Median length	7
Mean length	9.311284047
Min length	1

Characters and Unicode

Total characters	2393
Distinct characters	71
Distinct categories	6 ?
Distinct scripts	3 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	197 ?
Unique (%)	76.7%

Sample

1st row	Auto1
2nd row	Luxoft
3rd row	Zalando
4th row	Bonial International GmbH
5th row	Football app

Common Values

Value	Count	Frequency (%)
Zalando	7	0.2%
Auto1	5	0.2%
Booking.com	4	0.1%
Sixt	3	0.1%
ING	3	0.1%
Luxoft	3	0.1%
SAP	3	0.1%
Ingenico	2	0.1%
Yandex	2	0.1%
Here	2	0.1%
Other values (210)	223	7.4%
(Missing)	2752	91.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
gmbh	22	6.2%
auto1	9	2.5%
zalando	7	2.0%
group	7	2.0%
booking.com	4	1.1%
check24	4	1.1%
ing	4	1.1%
ag	4	1.1%
digital	3	0.8%
scout24	3	0.8%
Other values (246)	287	81.1%

Most occurring characters

Value	Count	Frequency (%)
e	196	8.2%
o	163	6.8%
a	155	6.5%
n	140	5.9%
t	135	5.6%
i	128	5.3%
r	121	5.1%
	115	4.8%
s	89	3.7%
l	88	3.7%
Other values (61)	1063	44.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1747	73.0%
Uppercase Letter	476	19.9%
Space Separator	115	4.8%
Decimal Number	34	1.4%
Other Punctuation	15	0.6%
Dash Punctuation	6	0.3%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
A	46	9.7%
S	46	9.7%
G	43	9.0%
H	32	6.7%
I	29	6.1%
T	28	5.9%
E	24	5.0%
C	23	4.8%
B	21	4.4%
D	21	4.4%
Other values (20)	163	34.2%

Lowercase Letter

Value	Count	Frequency (%)
e	196	11.2%
o	163	9.3%
a	155	8.9%
n	140	8.0%
t	135	7.7%
i	128	7.3%
r	121	6.9%
s	89	5.1%
l	88	5.0%
c	73	4.2%
Other values (20)	459	26.3%

Decimal Number

Value	Count	Frequency (%)
1	16	47.1%
2	7	20.6%
4	7	20.6%
3	2	5.9%
6	1	2.9%
0	1	2.9%

Other Punctuation

Value	Count	Frequency (%)
.	9	60.0%
&	4	26.7%
/	2	13.3%

Space Separator

Value	Count	Frequency (%)
	115	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2213	92.5%
Common	170	7.1%
Cyrillic	10	0.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	196	8.9%
o	163	7.4%
a	155	7.0%
n	140	6.3%
t	135	6.1%
i	128	5.8%
r	121	5.5%
s	89	4.0%
l	88	4.0%
c	73	3.3%
Other values (41)	925	41.8%

Common

Value	Count	Frequency (%)
	115	67.6%
1	16	9.4%
.	9	5.3%
2	7	4.1%
4	7	4.1%
-	6	3.5%
&	4	2.4%
/	2	1.2%
3	2	1.2%
6	1	0.6%

Cyrillic

Value	Count	Frequency (%)
ф	2	20.0%
Я	1	10.0%
д	1	10.0%
т	1	10.0%
в	1	10.0%
щ	1	10.0%
С	1	10.0%
П	1	10.0%
Ш	1	10.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2382	99.5%
Cyrillic	10	0.4%
Latin 1 Sup	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	196	8.2%
o	163	6.8%
a	155	6.5%
n	140	5.9%
t	135	5.7%
i	128	5.4%
r	121	5.1%
	115	4.8%
s	89	3.7%
l	88	3.7%
Other values (51)	1052	44.2%

Cyrillic

Value	Count	Frequency (%)
ф	2	20.0%
Я	1	10.0%
д	1	10.0%
т	1	10.0%
в	1	10.0%
щ	1	10.0%
С	1	10.0%
П	1	10.0%
Ш	1	10.0%

Latin 1 Sup

Value	Count	Frequency (%)
ó	1	100.0%

Company business sector
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	52
Distinct (%)	6.1%
Missing	2163
Missing (%)	71.9%
Memory size	23.6 KiB

Сommerce	205
Finance / Insurance	163
Transport	86
Manufacture	53
IT	44
Other values (47)	295

Length

Max length	23
Median length	9
Mean length	10.88179669
Min length	2

Characters and Unicode

Total characters	9206
Distinct characters	45
Distinct categories	5 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	19 ?
Unique (%)	2.2%

Sample

1st row	Tourism
2nd row	Scientific Activities
3rd row	Transport
4th row	Automotive
5th row	Сommerce

Common Values

Value	Count	Frequency (%)
Сommerce	205	6.8%
Finance / Insurance	163	5.4%
Transport	86	2.9%
Manufacture	53	1.8%
IT	44	1.5%
Health	37	1.2%
Tourism	36	1.2%
Software Development	30	1.0%
Scientific Activities	24	0.8%
Telecom	19	0.6%
Other values (42)	149	5.0%
(Missing)	2163	71.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
сommerce	205	16.4%
	163	13.1%
insurance	163	13.1%
finance	163	13.1%
transport	86	6.9%
manufacture	53	4.2%
it	44	3.5%
health	37	3.0%
tourism	36	2.9%
software	31	2.5%
Other values (54)	268	21.5%

Most occurring characters

Value	Count	Frequency (%)
e	1144	12.4%
n	950	10.3%
r	719	7.8%
c	712	7.7%
a	683	7.4%
m	528	5.7%
o	489	5.3%
i	473	5.1%
t	455	4.9%
	405	4.4%
Other values (35)	2648	28.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7490	81.4%
Uppercase Letter	1145	12.4%
Space Separator	405	4.4%
Other Punctuation	164	1.8%
Dash Punctuation	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1144	15.3%
n	950	12.7%
r	719	9.6%
c	712	9.5%
a	683	9.1%
m	528	7.0%
o	489	6.5%
i	473	6.3%
t	455	6.1%
s	363	4.8%
Other values (12)	974	13.0%

Uppercase Letter

Value	Count	Frequency (%)
С	213	18.6%
I	213	18.6%
T	193	16.9%
F	168	14.7%
M	72	6.3%
S	64	5.6%
A	52	4.5%
H	42	3.7%
D	37	3.2%
E	30	2.6%
Other values (9)	61	5.3%

Other Punctuation

Value	Count	Frequency (%)
/	163	99.4%
,	1	0.6%

Space Separator

Value	Count	Frequency (%)
	405	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8422	91.5%
Common	571	6.2%
Cyrillic	213	2.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1144	13.6%
n	950	11.3%
r	719	8.5%
c	712	8.5%
a	683	8.1%
m	528	6.3%
o	489	5.8%
i	473	5.6%
t	455	5.4%
s	363	4.3%
Other values (30)	1906	22.6%

Common

Value	Count	Frequency (%)
	405	70.9%
/	163	28.5%
-	2	0.4%
,	1	0.2%

Cyrillic

Value	Count	Frequency (%)
С	213	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8993	97.7%
Cyrillic	213	2.3%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1144	12.7%
n	950	10.6%
r	719	8.0%
c	712	7.9%
a	683	7.6%
m	528	5.9%
o	489	5.4%
i	473	5.3%
t	455	5.1%
	405	4.5%
Other values (34)	2435	27.1%

Cyrillic

Value	Count	Frequency (%)
С	213	100.0%

0
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing	3009
Missing (%)	100.0%
Memory size	23.6 KiB

Position
Categorical

HIGH CARDINALITY
MISSING

Distinct	397
Distinct (%)	53.9%
Missing	2272
Missing (%)	75.5%
Memory size	23.6 KiB

Java Developer	34
Software Engineer	29
Senior Software Engineer	16
QA	15
Software Developer	13
Other values (392)	630

Length

Max length	48
Median length	16
Mean length	16.01899593
Min length	2

Characters and Unicode

Total characters	11806
Distinct characters	79
Distinct categories	9 ?
Distinct scripts	3 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	298 ?
Unique (%)	40.4%

Sample

1st row	QA Ingenieur
2nd row	Senior PHP Magento developer
3rd row	Software Engineer
4th row	Senior Frontend Developer
5th row	UX Designer

Common Values

Value	Count	Frequency (%)
Java Developer	34	1.1%
Software Engineer	29	1.0%
Senior Software Engineer	16	0.5%
QA	15	0.5%
Software Developer	13	0.4%
Frontend Developer	13	0.4%
iOS Developer	12	0.4%
Data Scientist	11	0.4%
DevOps	11	0.4%
Android Developer	8	0.3%
Other values (387)	575	19.1%
(Missing)	2272	75.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
developer	302	18.7%
engineer	157	9.7%
software	124	7.7%
senior	96	5.9%
java	66	4.1%
qa	43	2.7%
data	35	2.2%
c	33	2.0%
devops	32	2.0%
manager	32	2.0%
Other values (200)	696	43.1%

Most occurring characters

Value	Count	Frequency (%)
e	1946	16.5%
r	960	8.1%
	944	8.0%
n	771	6.5%
o	733	6.2%
a	667	5.6%
t	529	4.5%
i	528	4.5%
l	449	3.8%
v	433	3.7%
Other values (69)	3846	32.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	9033	76.5%
Uppercase Letter	1684	14.3%
Space Separator	944	8.0%
Other Punctuation	60	0.5%
Math Symbol	40	0.3%
Open Punctuation	14	0.1%
Close Punctuation	14	0.1%
Dash Punctuation	11	0.1%
Decimal Number	6	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1946	21.5%
r	960	10.6%
n	771	8.5%
o	733	8.1%
a	667	7.4%
t	529	5.9%
i	528	5.8%
l	449	5.0%
v	433	4.8%
p	383	4.2%
Other values (30)	1634	18.1%

Uppercase Letter

Value	Count	Frequency (%)
S	334	19.8%
D	315	18.7%
E	179	10.6%
A	114	6.8%
P	108	6.4%
J	82	4.9%
T	68	4.0%
O	63	3.7%
C	61	3.6%
F	60	3.6%
Other values (14)	300	17.8%

Other Punctuation

Value	Count	Frequency (%)
.	26	43.3%
/	17	28.3%
#	14	23.3%
&	1	1.7%
\	1	1.7%
,	1	1.7%

Decimal Number

Value	Count	Frequency (%)
2	2	33.3%
3	2	33.3%
1	1	16.7%
0	1	16.7%

Space Separator

Value	Count	Frequency (%)
	944	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	11	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	14	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	14	100.0%

Math Symbol

Value	Count	Frequency (%)
+	40	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	10696	90.6%
Common	1089	9.2%
Cyrillic	21	0.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1946	18.2%
r	960	9.0%
n	771	7.2%
o	733	6.9%
a	667	6.2%
t	529	4.9%
i	528	4.9%
l	449	4.2%
v	433	4.0%
p	383	3.6%
Other values (38)	3297	30.8%

Cyrillic

Value	Count	Frequency (%)
у	3	14.3%
е	2	9.5%
с	2	9.5%
и	2	9.5%
в	1	4.8%
м	1	4.8%
д	1	4.8%
щ	1	4.8%
з	1	4.8%
к	1	4.8%
Other values (6)	6	28.6%

Common

Value	Count	Frequency (%)
	944	86.7%
+	40	3.7%
.	26	2.4%
/	17	1.6%
(	14	1.3%
)	14	1.3%
#	14	1.3%
-	11	1.0%
2	2	0.2%
3	2	0.2%
Other values (5)	5	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11785	99.8%
Cyrillic	21	0.2%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1946	16.5%
r	960	8.1%
	944	8.0%
n	771	6.5%
o	733	6.2%
a	667	5.7%
t	529	4.5%
i	528	4.5%
l	449	3.8%
v	433	3.7%
Other values (53)	3825	32.5%

Cyrillic

Value	Count	Frequency (%)
у	3	14.3%
е	2	9.5%
с	2	9.5%
и	2	9.5%
в	1	4.8%
м	1	4.8%
д	1	4.8%
щ	1	4.8%
з	1	4.8%
к	1	4.8%
Other values (6)	6	28.6%

Your level
Categorical

HIGH CORRELATION
MISSING

Distinct	3
Distinct (%)	0.4%
Missing	2266
Missing (%)	75.3%
Memory size	23.6 KiB

Senior	497
Middle	206
Junior	40

Length

Max length	6
Median length	6
Mean length	6
Min length	6

Characters and Unicode

Total characters	4458
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Senior
2nd row	Senior
3rd row	Senior
4th row	Senior
5th row	Senior

Common Values

Value	Count	Frequency (%)
Senior	497	16.5%
Middle	206	6.8%
Junior	40	1.3%
(Missing)	2266	75.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
senior	497	66.9%
middle	206	27.7%
junior	40	5.4%

Most occurring characters

Value	Count	Frequency (%)
i	743	16.7%
e	703	15.8%
n	537	12.0%
o	537	12.0%
r	537	12.0%
S	497	11.1%
d	412	9.2%
M	206	4.6%
l	206	4.6%
J	40	0.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3715	83.3%
Uppercase Letter	743	16.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
i	743	20.0%
e	703	18.9%
n	537	14.5%
o	537	14.5%
r	537	14.5%
d	412	11.1%
l	206	5.5%
u	40	1.1%

Uppercase Letter

Value	Count	Frequency (%)
S	497	66.9%
M	206	27.7%
J	40	5.4%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4458	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
i	743	16.7%
e	703	15.8%
n	537	12.0%
o	537	12.0%
r	537	12.0%
S	497	11.1%
d	412	9.2%
M	206	4.6%
l	206	4.6%
J	40	0.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4458	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
i	743	16.7%
e	703	15.8%
n	537	12.0%
o	537	12.0%
r	537	12.0%
S	497	11.1%
d	412	9.2%
M	206	4.6%
l	206	4.6%
J	40	0.9%

Current Salary
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	139
Distinct (%)	18.5%
Missing	2259
Missing (%)	75.1%
Infinite	0
Infinite (%)	0.0%
Mean	68381.76533

Minimum	10300
Maximum	200000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	10300
5-th percentile	40000
Q1	57000
median	65000
Q3	75000
95-th percentile	110000
Maximum	200000
Range	189700
Interquartile range (IQR)	18000

Descriptive statistics

Standard deviation	21196.30656
Coefficient of variation (CV)	0.3099701573
Kurtosis	5.178434622
Mean	68381.76533
Median Absolute Deviation (MAD)	10000
Skewness	1.414854159
Sum	51286324
Variance	449283411.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
60000	73	2.4%
65000	68	2.3%
70000	50	1.7%
75000	43	1.4%
55000	30	1.0%
80000	28	0.9%
72000	23	0.8%
50000	20	0.7%
68000	20	0.7%
90000	19	0.6%
Other values (129)	376	12.5%
(Missing)	2259	75.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
10300	1	< 0.1%
13000	1	< 0.1%
15000	1	< 0.1%
17532	1	< 0.1%
19200	1	< 0.1%
20400	1	< 0.1%
23162	1	< 0.1%
24000	4	0.1%
27000	1	< 0.1%
30000	5	0.2%

Value	Count	Frequency (%)
200000	1	< 0.1%
180000	1	< 0.1%
176000	1	< 0.1%
165000	1	< 0.1%
150000	1	< 0.1%
140000	2	0.1%
138000	1	< 0.1%
132250	1	< 0.1%
132000	1	< 0.1%
130000	6	0.2%

Salary one year ago
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	125
Distinct (%)	21.0%
Missing	2413
Missing (%)	80.2%
Infinite	0
Infinite (%)	0.0%
Mean	62187.27852

Minimum	10001
Maximum	200000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	10001
5-th percentile	30000
Q1	52000
median	60000
Q3	70000
95-th percentile	95250
Maximum	200000
Range	189999
Interquartile range (IQR)	18000

Descriptive statistics

Standard deviation	20163.00866
Coefficient of variation (CV)	0.3242304398
Kurtosis	4.720050742
Mean	62187.27852
Median Absolute Deviation (MAD)	10000
Skewness	0.9022019418
Sum	37063618
Variance	406546918.3
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
65000	57	1.9%
55000	48	1.6%
60000	42	1.4%
70000	26	0.9%
50000	24	0.8%
75000	19	0.6%
58000	18	0.6%
80000	18	0.6%
45000	15	0.5%
68000	15	0.5%
Other values (115)	314	10.4%
(Missing)	2413	80.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
10001	1	< 0.1%
10300	1	< 0.1%
10500	2	0.1%
10800	1	< 0.1%
11520	1	< 0.1%
12000	1	< 0.1%
13000	1	< 0.1%
14000	1	< 0.1%
14400	1	< 0.1%
16500	1	< 0.1%

Value	Count	Frequency (%)
200000	1	< 0.1%
130000	2	0.1%
129000	1	< 0.1%
125000	2	0.1%
124000	1	< 0.1%
123000	1	< 0.1%
120000	3	0.1%
119000	1	< 0.1%
115000	2	0.1%
110000	5	0.2%

Salary two years ago
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	105
Distinct (%)	22.7%
Missing	2546
Missing (%)	84.6%
Infinite	0
Infinite (%)	0.0%
Mean	58013.47516

Minimum	10001
Maximum	150000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	23.6 KiB

Quantile statistics

Minimum	10001
5-th percentile	22200
Q1	48000
median	56000
Q3	67000
95-th percentile	95000
Maximum	150000
Range	139999
Interquartile range (IQR)	19000

Descriptive statistics

Standard deviation	20413.04891
Coefficient of variation (CV)	0.3518673696
Kurtosis	1.95291779
Mean	58013.47516
Median Absolute Deviation (MAD)	10000
Skewness	0.5754506612
Sum	26860239
Variance	416692565.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
60000	38	1.3%
55000	36	1.2%
65000	31	1.0%
50000	30	1.0%
70000	17	0.6%
45000	16	0.5%
40000	16	0.5%
54000	14	0.5%
48000	14	0.5%
75000	13	0.4%
Other values (95)	238	7.9%
(Missing)	2546	84.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
10001	4	0.1%
10300	1	< 0.1%
10500	1	< 0.1%
10800	2	0.1%
12000	1	< 0.1%
13000	2	0.1%
15000	3	0.1%
16000	1	< 0.1%
16500	1	< 0.1%
17000	1	< 0.1%

Value	Count	Frequency (%)
150000	1	< 0.1%
130000	1	< 0.1%
125000	1	< 0.1%
124000	1	< 0.1%
120000	2	0.1%
119000	1	< 0.1%
118000	1	< 0.1%
115000	2	0.1%
110000	1	< 0.1%
105000	2	0.1%

Are you getting any Stock Options?
Boolean

MISSING

Distinct	2
Distinct (%)	0.3%
Missing	2267
Missing (%)	75.3%
Memory size	6.0 KiB

False	587
True	155
(Missing)	2267

Common Values
Chart

Value	Count	Frequency (%)
False	587	19.5%
True	155	5.2%
(Missing)	2267	75.3%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	df_index	Timestamp	Age	Gender	City	Position	Total years of experience	Years of experience in Germany	Seniority level	Your main technology / programming language	Other technologies/programming languages you use often	Yearly brutto salary (without bonus and stocks) in EUR	Yearly bonus + stocks in EUR	Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country	Annual bonus+stocks one year ago. Only answer if staying in same country	Number of vacation days	Employment status	Сontract duration	Main language at work	Company size	Company type	Have you lost your job due to the coronavirus outbreak?	Have you been forced to have a shorter working week (Kurzarbeit)? If yes, how many hours per week	Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR	Zeitstempel	Position (without seniority)	Years of experience	Yearly brutto salary (without bonus and stocks)	Yearly bonus	Yearly stocks	Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country	Yearly bonus one year ago. Only answer if staying in same country	Yearly stocks one year ago. Only answer if staying in same country	Number of home office days per month	Company name	Company business sector	0	Position	Your level	Current Salary	Salary one year ago	Salary two years ago	Are you getting any Stock Options?
0	0	24/11/2020 11:14:15	26.0	Male	Munich	Software Engineer	5	3	Senior	TypeScript	Kotlin, Javascript / Typescript	80000.0	5000	75000.0	10000	30	Full-time employee	Unlimited contract	English	51-100	Product	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1	24/11/2020 11:14:16	26.0	Male	Berlin	Backend Developer	7	4	Senior	Ruby	NaN	80000.0	NaN	82000.0	5000	28	Full-time employee	Unlimited contract	English	101-1000	Product	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2	24/11/2020 11:14:21	29.0	Male	Berlin	Software Engineer	12	6	Lead	Javascript / Typescript	Javascript / Typescript, Docker	120000.0	120000	100000.0	100000	30	Self-employed (freelancer)	Temporary contract	English	101-1000	Product	Yes	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	3	24/11/2020 11:15:24	28.0	Male	Berlin	Frontend Developer	4	1	Junior	Javascript	NaN	54000.0	NaN	NaN	NaN	24	Full-time employee	Unlimited contract	English	51-100	Startup	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	4	24/11/2020 11:15:46	37.0	Male	Berlin	Backend Developer	17	6	Senior	C# .NET	.NET, SQL, AWS, Docker	62000.0	NaN	62000.0	NaN	29	Full-time employee	Unlimited contract	English	101-1000	Product	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	5	24/11/2020 11:15:53	32.0	Male	Berlin	DevOps	5	1	Senior	AWS, GCP, Python,K8s	Python, AWS, Google Cloud, Kubernetes, Docker	76000.0	5000	76000.0	5000	30	Full-time employee	Unlimited contract	English	11-50	Startup	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6	6	24/11/2020 11:16:35	37.0	Male	Berlin	Frontend Developer	6	0.4	Middle	Javascript	NaN	57000.0	NaN	NaN	NaN	24	Full-time employee	Unlimited contract	English	11-50	Product	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	7	24/11/2020 11:16:44	24.0	Male	Berlin	Frontend Developer	5	1	Senior	Typescript	Javascript / Typescript	65000.0	NaN	65000.0	NaN	27	Full-time employee	Unlimited contract	English	1000+	Product	No	0.0	600	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	8	24/11/2020 11:17:24	29.0	Male	Berlin	Backend Developer	8	2	Senior	PHP	SQL, AWS, Docker	56000.0	NaN	55000.0	NaN	28	Full-time employee	Unlimited contract	English	101-1000	Product	No	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9	9	24/11/2020 11:17:50	35.0	Male	Berlin	Software Engineer	15	3	Lead	Java	NaN	95000.0	NaN	90000.0	NaN	30	Full-time employee	Unlimited contract	English	101-1000	Product	No	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Last rows

	df_index	Timestamp	Age	Gender	City	Position	Total years of experience	Years of experience in Germany	Seniority level	Your main technology / programming language	Other technologies/programming languages you use often	Yearly brutto salary (without bonus and stocks) in EUR	Yearly bonus + stocks in EUR	Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country	Annual bonus+stocks one year ago. Only answer if staying in same country	Number of vacation days	Employment status	Сontract duration	Main language at work	Company size	Company type	Have you lost your job due to the coronavirus outbreak?	Have you been forced to have a shorter working week (Kurzarbeit)? If yes, how many hours per week	Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR	Zeitstempel	Position (without seniority)	Years of experience	Yearly brutto salary (without bonus and stocks)	Yearly bonus	Yearly stocks	Yearly brutto salary (without bonus and stocks) one year ago. Only answer if staying in same country	Yearly bonus one year ago. Only answer if staying in same country	Yearly stocks one year ago. Only answer if staying in same country	Number of home office days per month	Company name	Company business sector	0	Position	Your level	Current Salary	Salary one year ago	Salary two years ago	Are you getting any Stock Options?
2999	755	06/01/2020 13:38:05	30.0	M	München	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Deutsch	100-1000	Agency	NaN	NaN	NaN	NaN	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Softwareentwickler	Middle	75000.0	50000.0	NaN	No
3000	756	05/03/2020 16:50:43	52.0	M	Köln	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	English	1000+	Agency	NaN	NaN	NaN	NaN	NaN	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	IT Engineer	Middle	63000.0	80000.0	80000.0	No
3001	757	11/03/2020 23:23:19	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3002	758	31/05/2020 16:28:15	NaN	M	Berlin	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3003	759	31/05/2020 16:28:23	NaN	M	Berlin	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3004	760	03/06/2020 20:12:51	40.0	M	Köln	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Deutsch	10-50	Product	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Java Developer junior	Junior	44000.0	40000.0	40000.0	Yes
3005	761	28/07/2020 04:03:13	NaN	M	Köln	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Deutsch	10-50	Product	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	E.g. C# Developer	Junior	45000.0	40000.0	40000.0	Yes
3006	762	28/07/2020 04:03:20	NaN	M	Köln	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Deutsch	10-50	Product	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	E.g. C# Developer	Junior	45000.0	40000.0	40000.0	Yes
3007	763	26/08/2020 09:06:44	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3008	764	21/09/2020 01:47:10	31.0	F	München	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	English	1000+	Product	NaN	NaN	NaN	NaN	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Pm	Senior	110000.0	NaN	NaN	No

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Other Punctuation

Space Separator

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Dash Punctuation

Space Separator

Other Punctuation

Open Punctuation

Close Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Latin 1 Sup

Latin Ext A

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Space Separator

Open Punctuation

Close Punctuation

Math Symbol

Dash Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Cyrillic

Most occurring blocks

Most frequent character per block

ASCII

Cyrillic

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Decimal Number